Scaffold v0.1.0: corpus collector, methodology spec, docs-enforcement CI
- ingest: 20 outlets' politics feeds -> full text -> SQLite w/ SHA-256 fingerprints - METHODOLOGY.md: 10 decision blocks (decision/rationale+sources/alternatives/failure modes) - docs: research foundations, plain-language walkthrough (CI-gated <= grade 10), glossary, ADR-0001, generated outlets table - CI: pytest+ruff, docs gates (readability, glossary coverage, decision-block lint, outlets-table freshness), code/docs sync gate - systemd user timer ingests every 6h (repo-external, documented in README quickstart)
This commit is contained in:
flan
@@ -0,0 +1,40 @@
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name: ci
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on:
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push:
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branches: [main, dev]
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pull_request:
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: astral-sh/setup-uv@v5
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- run: uv sync --dev
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- run: uv run ruff check .
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- name: tests (includes docs gates - readability, glossary, decision-block lint)
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run: uv run pytest -q
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docs-sync:
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# Code and config may not change without a docs change in the same range.
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: determine range and check
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env:
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EVENT: ${{ github.event_name }}
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BEFORE: ${{ github.event.before }}
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SHA: ${{ github.sha }}
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BASE_REF: ${{ github.base_ref }}
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run: |
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if [[ "$EVENT" == "pull_request" ]]; then
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range="origin/${BASE_REF}...HEAD"
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elif [[ "$BEFORE" =~ ^0+$ ]]; then
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echo "new branch push; skipping docs-sync"; exit 0
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else
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range="${BEFORE}..${SHA}"
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fi
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bash scripts/check_docs_sync.sh "$range"
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+10
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# collected corpus stays local (copyright: we publish manifests, not text)
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data/
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# python
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__pycache__/
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*.pyc
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.venv/
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dist/
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.pytest_cache/
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.ruff_cache/
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# Changelog
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All notable changes to this project are documented here. Format follows
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[Keep a Changelog](https://keepachangelog.com/en/1.1.0/); versioning follows
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[SemVer](https://semver.org). Any change that could alter published ratings
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requires a version bump and, if it changes methodology, a decision record in
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`docs/decisions/`.
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## [Unreleased]
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## [0.1.0] - 2026-07-10
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### Added
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- Project scaffold: uv-managed Python 3.13 package, MIT license, CI.
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- Corpus collector (`tiltmeter ingest`): polls 20 US outlets' politics RSS
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feeds, fetches full article text once per URL, stores to SQLite with SHA-256
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content fingerprints. `tiltmeter status` reports per-outlet counts.
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- Methodology specification (`METHODOLOGY.md`): ten decision blocks
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(D1–D10), each with decision, sourced rationale, alternatives, and failure
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modes; format machine-checked in CI.
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- Foundational literature review (`docs/research.md`), venue-verified.
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- Plain-language walkthrough (`docs/how-it-works.md`), CI-gated to ≤ grade-10
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reading level; glossary (`docs/glossary.md`).
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- ADR-0001: story-selection ideal points with congressional-language anchor.
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- Docs enforcement in CI: readability gate, glossary coverage, decision-block
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and ADR format lint, generated outlets table freshness, code/docs sync gate.
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### Changed
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- Outlet sample swaps against the planned list (feed availability,
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2026-07-10): AP → Christian Science Monitor (AP has no public RSS; 401),
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WSJ politics feed → WSJ world news feed (politics feed dead; Opinion feed
|
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deliberately not used — news and opinion are rated separately by every
|
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incumbent rater).
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MIT License
|
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|
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Copyright (c) 2026 flan <flan@arch.fyi>
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||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
+245
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# Methodology
|
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This document is the technical specification of how tiltmeter computes political-lean
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ratings. It is the product: the numbers are only as good as this document is honest.
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||||
|
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Every design choice below is a numbered decision block with four mandatory fields —
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||||
**Decision**, **Rationale** (with sources), **Alternatives considered**, and **Failure
|
||||
modes**. This format is machine-checked by `tests/test_docs.py`. Changing any decision
|
||||
requires a new record in `docs/decisions/`, a CHANGELOG entry, and a version bump — the
|
||||
methodology cannot move quietly.
|
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|
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Plain-language version: [docs/how-it-works.md](docs/how-it-works.md).
|
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Literature foundations: [docs/research.md](docs/research.md).
|
||||
Term definitions: [docs/glossary.md](docs/glossary.md).
|
||||
|
||||
Choices that are pragmatic rather than literature-backed are explicitly labeled
|
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**Tunable** and are covered by the published parameter sensitivity analysis (D7)
|
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rather than dressed up as principled.
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|
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---
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## D1. Ratings come from a reproducible pipeline, not editorial judgment
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**Decision**: Every rating is computed by open code running on an open, content-hashed
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corpus snapshot, with pinned dependencies and fixed random seeds. Anyone can rerun the
|
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pipeline and obtain the identical output. No human judgment enters the scoring path.
|
||||
|
||||
**Rationale**: The incumbent raters (AllSides, Ad Fontes, MBFC) all rest on structured
|
||||
human judgment — balanced panels, blind surveys, rubrics (see
|
||||
[docs/research.md §2](docs/research.md)). Their answer to bias is balancing the panel;
|
||||
ours is removing it. A reproducible method shifts disputes from "you are biased" to
|
||||
"here is the code and data" — the dispute becomes checkable. Validation discipline for
|
||||
text-based measures follows Grimmer & Stewart (2013), *Text as Data*, Political
|
||||
Analysis.
|
||||
|
||||
**Alternatives considered**: Human ratings with published evidence worksheets
|
||||
(AllSides-style transparency) — scales poorly and invites governance brigading. Hybrid
|
||||
algorithmic-plus-override — inherits the governance problem the moment the first
|
||||
override lands.
|
||||
|
||||
**Failure modes**: Reproducible is not the same as correct — a deterministic pipeline
|
||||
can be deterministically wrong, which is why validation (D7) is a gate, not a
|
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formality. Method choices themselves can encode bias; that risk is handled by
|
||||
citing each choice to prior literature and publishing sensitivity analyses.
|
||||
|
||||
## D2. Version 1 rates political lean only
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**Decision**: One axis: political lean, expressed on a [-1, +1] scale. Reliability
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and factuality are out of scope for v1.
|
||||
|
||||
**Rationale**: Lean is the axis with the strongest measurement tradition (Puglisi &
|
||||
Snyder 2015, *Empirical Studies of Media Bias*, Handbook of Media Economics) and the
|
||||
best available validation targets (both AllSides and Ad Fontes publish lean ratings).
|
||||
Reliability requires corrections tracking and fact-check cross-referencing — a
|
||||
different, harder pipeline.
|
||||
|
||||
**Alternatives considered**: Two-axis lean+reliability (Ad Fontes chart) — doubles v1
|
||||
scope. Reliability only — less contentious but hardest to compute and not what
|
||||
blindspot-style analysis needs.
|
||||
|
||||
**Failure modes**: A lean-only score can be misread as a quality score. Every published
|
||||
artifact states that lean ≠ reliability.
|
||||
|
||||
## D3. The v1 signal is story selection, framed as ideal-point estimation
|
||||
|
||||
**Decision**: Measure *what outlets choose to cover*. Articles from all outlets are
|
||||
embedded (all-MiniLM-L6-v2 sentence embeddings of headline + lede) and grouped into
|
||||
cross-outlet story clusters. The outlet×story coverage matrix is scaled by
|
||||
correspondence analysis; the first principal axis is the candidate lean dimension.
|
||||
Framing: this is roll-call analysis where outlets are legislators and coverage
|
||||
decisions are votes.
|
||||
|
||||
**Rationale**: Selection (gatekeeping) bias is a real and measurable channel:
|
||||
D'Alessio & Allen (2000, J. Communication) meta-analytic taxonomy; Budak, Goel & Rao
|
||||
(2016, Public Opinion Quarterly) found outlet bias manifests largely through issue
|
||||
selection; Rönnback, Emmery & Brighton (2025, PLOS One) found coverage features the
|
||||
most informative for outlet-level bias prediction. The scaling method sits in the
|
||||
ideal-point tradition (Poole & Rosenthal 1985, AJPS; unsupervised text scaling:
|
||||
Slapin & Proksch 2008, AJPS "Wordfish"). It requires no training data, no lexicon,
|
||||
and no LLM judgment.
|
||||
|
||||
**Alternatives considered**: Language/framing similarity to congressional speech
|
||||
(Gentzkow & Shapiro 2010, Econometrica) — planned as signal S2 in v0.2, not v1,
|
||||
because it requires the corrected high-dimensional estimator of Gentzkow, Shapiro &
|
||||
Taddy (2019, Econometrica) to avoid severe finite-sample bias. Audience-based scores
|
||||
(Bakshy, Messing & Adamic 2015, Science; Barberá 2015) — platform data access is now
|
||||
gated. LLM-as-judge — excluded by D8.
|
||||
|
||||
**Failure modes**: The first principal axis may capture topic mix (business vs.
|
||||
lifestyle) rather than politics — mitigated by politics-section feeds (D9) and by
|
||||
publishing the story clusters behind every score so the axis is inspectable. Small
|
||||
outlet samples make the axis unstable — mitigated by the snapshot window length and
|
||||
bootstrap confidence intervals (D6). **Tunable**: embedding model, clustering
|
||||
threshold — both in the sensitivity sweep (D7).
|
||||
|
||||
## D4. Twenty outlets, deliberately spread, chosen once and openly
|
||||
|
||||
**Decision**: v1 covers the 20 US outlets in `config/outlets.yaml`, selected to span
|
||||
the spectrum from Mother Jones/The Nation to Breitbart/Newsmax, with
|
||||
politics-section RSS feeds. Incumbent-rater ratings were consulted for *sample
|
||||
selection only* — they are never an input to scoring.
|
||||
|
||||
**Rationale**: A proof of concept needs a sample where the expected ordering is
|
||||
uncontroversial enough that failure to recover it falsifies the method. Feed
|
||||
availability was verified 2026-07-10; two swaps from the original list (AP → CS
|
||||
Monitor, dead feed; WSJ politics → WSJ world news feed) are recorded in CHANGELOG.md.
|
||||
|
||||
**Alternatives considered**: ~100-outlet US national coverage — more infrastructure
|
||||
before the method is proven. Including international outlets — the left-right axis is
|
||||
US-calibrated and ground truth thins out.
|
||||
|
||||
**Failure modes**: Sample selection is itself a choice with bias potential; the
|
||||
selection criteria are stated in `config/outlets.yaml` comments, and the sample is
|
||||
frozen for v1 so results cannot be cherry-picked by adding/removing outlets
|
||||
post hoc.
|
||||
|
||||
## D5. The axis is oriented by congressional language, not by assumed outlet leans
|
||||
|
||||
**Decision**: No outlet's lean is assumed anywhere in the pipeline. The unsupervised
|
||||
axis from D3 gets its sign (which end is "left") from an external reference corpus:
|
||||
recent Congressional Record speeches (govinfo.gov bulk data), with party membership
|
||||
and DW-NOMINATE scores from voteview.com. Whichever axis pole is closer to Democratic
|
||||
vs. Republican language determines orientation. The published reference frame is
|
||||
explicit: *lean relative to contemporary US congressional party discourse*.
|
||||
|
||||
**Rationale**: Lean is relational — every method in the literature defines it against
|
||||
a reference population. Politicians are the standard anchor because their ideology is
|
||||
a matter of public record (roll-call votes), not a rating: Gentzkow & Shapiro (2010,
|
||||
Econometrica); Poole & Rosenthal's DW-NOMINATE. Anchoring to politicians also keeps
|
||||
validation (D7) non-circular: no rater data enters the pipeline.
|
||||
|
||||
**Alternatives considered**: Declared anchor outlets ("Mother Jones = left") — one
|
||||
editorial bit, but exactly the kind of judgment this project exists to remove.
|
||||
Audience-based orientation — gated platform data.
|
||||
|
||||
**Failure modes**: Congressional discourse defines the center as the US two-party
|
||||
midpoint; positions outside that frame (or drift of the frame itself over time) are
|
||||
invisible to the measure. This is stated, not hidden: the frame is part of every
|
||||
release's metadata.
|
||||
|
||||
## D6. Scores are time series with uncertainty, never permanent labels
|
||||
|
||||
**Decision**: Every published score is attached to a corpus snapshot window and
|
||||
carries a 95% bootstrap confidence interval (resampling over story clusters).
|
||||
tiltmeter publishes no undated, uncertainty-free outlet labels.
|
||||
|
||||
**Rationale**: Outlet lean drifts, and even incumbent raters disagree with each other
|
||||
sharply — MBFC vs. audience-derived labels agree only 46%, two human-annotated
|
||||
sources 57% (Rönnback et al. 2025, PLOS One). Penn's Media Bias Detector (CHI 2025)
|
||||
adopts the same dynamic-view stance. Publishing uncertainty is what distinguishes a
|
||||
measurement from a verdict.
|
||||
|
||||
**Alternatives considered**: Static labels (the incumbent-rater product shape) —
|
||||
misrepresents both drift and measurement error.
|
||||
|
||||
**Failure modes**: Time series with CIs are harder for laymen to read than a single
|
||||
label — mitigated by the evidence pages (D10) and the plain-language docs layer.
|
||||
|
||||
## D7. Validation against incumbent raters is a gate, with published sensitivity
|
||||
|
||||
**Decision**: `config/reference_ratings.yaml` holds published AllSides (5-point,
|
||||
mapped to −2..+2) and Ad Fontes (numeric) ratings with retrieval dates, used for
|
||||
validation only. The v1 gate: Spearman rank correlation ρ ≥ 0.7 against both raters
|
||||
over the 20-outlet sample. Every release also publishes a parameter sensitivity
|
||||
sweep: how much ratings move under alternative tunables (clustering threshold,
|
||||
embedding model, window length).
|
||||
|
||||
**Rationale**: Agreement with independent human raters is the standard external check
|
||||
for text-based measures (Grimmer & Stewart 2013). The bar is deliberately below
|
||||
perfect: given 46–57% inter-rater agreement in adjacent work, near-perfect
|
||||
correlation with any one rater would indicate overfitting to that rater, not truth.
|
||||
Fair-use note: reference ratings are facts (a rating value on a date), stored with
|
||||
retrieval dates, used only for validation.
|
||||
|
||||
**Alternatives considered**: No external validation ("the method is principled,
|
||||
trust it") — indistinguishable from the incumbents' posture. Validating against
|
||||
audience data — gated.
|
||||
|
||||
**Failure modes**: ρ ≥ 0.7 on 20 outlets has wide confidence bounds itself; the
|
||||
validation report states the n and the CI on ρ. Failing the gate stops scaling — the
|
||||
documented response is to iterate the method, not the sample.
|
||||
|
||||
## D8. No LLM judgment in the scoring path
|
||||
|
||||
**Decision**: Large language models are never asked to judge bias, lean, framing, or
|
||||
quality anywhere in the pipeline. Embedding models are permitted for similarity
|
||||
computation only (D3), pinned by exact version.
|
||||
|
||||
**Rationale**: LLMs carry measurable political lean themselves: Rozado (2024, PLOS
|
||||
One, "The political preferences of LLMs" — 11 orientation tests × 24 models);
|
||||
Santurkar et al. (2023, ICML, "Whose Opinions Do Language Models Reflect?").
|
||||
Piping the news through a judge with its own lean would relocate the bias problem,
|
||||
not solve it. Small pinned embedding models keep runs reproducible in a way
|
||||
API-served LLM judgments are not.
|
||||
|
||||
**Alternatives considered**: LLM-as-judge with audited prompts — cheaper per article
|
||||
than any alternative, but unreproducible across model versions and lean-contaminated.
|
||||
Penn's Media Bias Detector accepts this trade; we don't.
|
||||
|
||||
**Failure modes**: Embeddings are not judgment-free either — they encode training
|
||||
distribution biases. The mitigation is that embeddings only *group similar text*
|
||||
here; the lean axis and its orientation come from coverage structure and public
|
||||
political records, and the embedding model is a published tunable in the
|
||||
sensitivity sweep (D7).
|
||||
|
||||
## D9. The corpus is politics-section RSS, stored locally, published as manifests
|
||||
|
||||
**Decision**: Ingestion polls each outlet's politics-section RSS feed (general feed
|
||||
where none exists), fetches full article text once per URL, and stores it locally.
|
||||
Published corpus artifacts are manifests — URL, headline, outlet, timestamp, SHA-256
|
||||
content hash per article — not full text.
|
||||
|
||||
**Rationale**: Politics-section feeds reduce the topic-mix confound in D3. Manifests
|
||||
let anyone re-fetch and hash-verify the exact corpus without tiltmeter
|
||||
redistributing copyrighted article text. Headline+link is the same posture
|
||||
aggregators take, and readers are always sent to the source.
|
||||
|
||||
**Alternatives considered**: Redistributing full text (clean reproducibility, clear
|
||||
copyright violation). Third-party corpora like GDELT (Rönnback et al. 2025 use it) —
|
||||
convenient but inserts an unauditable dependency between the outlet and our data.
|
||||
|
||||
**Failure modes**: Articles edited after our fetch won't match our stored hash —
|
||||
manifests carry the fetch timestamp, and hash mismatches on re-fetch are reported as
|
||||
exactly that. Feeds die (two already did, see D4) — per-outlet ingest counts are
|
||||
monitored and chronic failures trigger a documented swap.
|
||||
|
||||
## D10. Every number is traceable to evidence a layman can read
|
||||
|
||||
**Decision**: Every published score links to an evidence page: the story clusters
|
||||
that drove the outlet's position with example headlines side by side, its
|
||||
nearest-neighbor outlets, the confidence interval, the corpus snapshot ID, and the
|
||||
pipeline version. Reproducing any release must be a documented ≤3-command path ending
|
||||
in a hash-verified `ratings.json`.
|
||||
|
||||
**Rationale**: This is the project's core value made concrete (see README). A layman
|
||||
asking "why is this outlet here?" should see actual headlines, not eigenvectors.
|
||||
Auditability that requires a statistics degree is not auditability for the public
|
||||
this project serves.
|
||||
|
||||
**Alternatives considered**: Publishing scores + code only — auditable in principle,
|
||||
by almost nobody in practice.
|
||||
|
||||
**Failure modes**: Evidence pages could be cherry-picked — they are generated by the
|
||||
same deterministic pipeline as the scores (`report` module), never hand-edited, and
|
||||
regenerating them is part of the ≤3-command reproduction path.
|
||||
@@ -0,0 +1,10 @@
|
||||
tiltmeter — development disclosure
|
||||
|
||||
This project is developed with substantial AI assistance (Anthropic Claude),
|
||||
under human direction and review. Methodology decisions are recorded in
|
||||
docs/decisions/ with their rationale and sources regardless of who or what
|
||||
drafted them; the auditability guarantees in METHODOLOGY.md are enforced by
|
||||
CI, not by trust in any author, human or otherwise.
|
||||
|
||||
Consistent with the project's disclosure policy, AI use is declared here in
|
||||
the repository rather than in individual commit messages.
|
||||
@@ -0,0 +1,66 @@
|
||||
# tiltmeter
|
||||
|
||||
**Auditable political-lean ratings for news outlets.** A tiltmeter is an instrument
|
||||
that measures the tilt of the ground — this one measures the tilt of the news.
|
||||
|
||||
Commercial bias raters (AllSides, Ad Fontes, Media Bias/Fact Check) sell outlet
|
||||
ratings produced by human panels behind closed doors. You can't check their work.
|
||||
tiltmeter is the opposite bet: ratings computed by open code from open data, where
|
||||
every number can be re-derived, every methodology choice is cited to published
|
||||
research, and every score links to the actual headlines that produced it.
|
||||
|
||||
**Do not trust us. Check.**
|
||||
|
||||
## How it works (one paragraph)
|
||||
|
||||
tiltmeter watches which stories each of 20 US outlets chooses to cover, via their
|
||||
public politics feeds. Outlets making similar coverage choices land near each other
|
||||
on a map — a seating chart built from the outlets' own behavior, with no one voting
|
||||
on it. Which side of the map is "left" is decided by public records, not opinion:
|
||||
one pole's coverage sits closer to Democratic congressional speech, the other to
|
||||
Republican. Each outlet gets a dated score from −1 to +1 with an honest error range.
|
||||
Plain-language walkthrough: [docs/how-it-works.md](docs/how-it-works.md). Full
|
||||
technical spec with sources: [METHODOLOGY.md](METHODOLOGY.md).
|
||||
|
||||
## Status
|
||||
|
||||
Pre-alpha (v0.1, milestone M1). The corpus collector is running; scoring lands in
|
||||
M2; the first validated ratings release requires ≥2 weeks of corpus (M3). Nothing
|
||||
here is a usable rating yet.
|
||||
|
||||
## Quickstart
|
||||
|
||||
```sh
|
||||
uv sync # install
|
||||
uv run tiltmeter ingest # poll all 20 outlet feeds once
|
||||
uv run tiltmeter status # article counts per outlet
|
||||
```
|
||||
|
||||
## The auditability contract
|
||||
|
||||
- **Reproducible**: same snapshot + same version ⇒ byte-identical ratings. Anyone
|
||||
can verify a release in ≤3 commands.
|
||||
- **Sourced**: every methodology decision in [METHODOLOGY.md](METHODOLOGY.md) cites
|
||||
the research behind it ([literature review](docs/research.md)).
|
||||
- **Traceable**: every score links to an evidence page of real headlines.
|
||||
- **Versioned**: methodology changes require a [decision record](docs/decisions/),
|
||||
a CHANGELOG entry, and a version bump. The method cannot move quietly.
|
||||
- **Readable**: the plain-language docs are CI-gated to a grade-10 reading level.
|
||||
Jargon must be defined in the [glossary](docs/glossary.md).
|
||||
|
||||
## Project layout
|
||||
|
||||
| Path | What it is |
|
||||
|---|---|
|
||||
| `METHODOLOGY.md` | The product: every design decision, with sources |
|
||||
| `docs/how-it-works.md` | The same thing, for humans in a hurry |
|
||||
| `docs/research.md` | The literature this stands on |
|
||||
| `docs/decisions/` | Numbered methodology decision records (ADRs) |
|
||||
| `config/outlets.yaml` | The 20 outlets and their feeds |
|
||||
| `src/tiltmeter/` | The pipeline (each module states its question in plain language) |
|
||||
| `releases/` | Corpus manifests and ratings, per release |
|
||||
|
||||
## License
|
||||
|
||||
Code: [MIT](LICENSE). Published ratings data: CC-BY-4.0. See [NOTICE](NOTICE) for
|
||||
development disclosure.
|
||||
@@ -0,0 +1,76 @@
|
||||
# Outlets tiltmeter collects from.
|
||||
#
|
||||
# Selection criteria (see METHODOLOGY.md, decision D4):
|
||||
# - ~20 US outlets deliberately spread across the spectrum as rated by the
|
||||
# incumbent raters (used for SELECTION only, never as pipeline input)
|
||||
# - politics-section feed preferred where one exists, to reduce the
|
||||
# topic-mix confound (sports/lifestyle coverage polluting the signal)
|
||||
# - feed must actually work; broken outlets get swapped for a
|
||||
# same-region-of-spectrum alternative, recorded in CHANGELOG.md
|
||||
#
|
||||
# Fields: name (canonical id), feed (RSS/Atom URL), homepage.
|
||||
|
||||
outlets:
|
||||
- name: cs-monitor
|
||||
homepage: https://www.csmonitor.com
|
||||
feed: https://rss.csmonitor.com/feeds/politics
|
||||
- name: pbs-newshour
|
||||
homepage: https://www.pbs.org/newshour
|
||||
feed: https://www.pbs.org/newshour/feeds/rss/politics
|
||||
- name: npr
|
||||
homepage: https://www.npr.org
|
||||
feed: https://feeds.npr.org/1014/rss.xml
|
||||
- name: axios
|
||||
homepage: https://www.axios.com
|
||||
feed: https://api.axios.com/feed/
|
||||
- name: the-hill
|
||||
homepage: https://thehill.com
|
||||
feed: https://thehill.com/homenews/feed/
|
||||
- name: nytimes
|
||||
homepage: https://www.nytimes.com
|
||||
feed: https://rss.nytimes.com/services/xml/rss/nyt/Politics.xml
|
||||
- name: washington-post
|
||||
homepage: https://www.washingtonpost.com
|
||||
feed: https://feeds.washingtonpost.com/rss/politics
|
||||
- name: cnn
|
||||
homepage: https://www.cnn.com
|
||||
feed: http://rss.cnn.com/rss/cnn_allpolitics.rss
|
||||
- name: nbc-news
|
||||
homepage: https://www.nbcnews.com
|
||||
feed: https://feeds.nbcnews.com/nbcnews/public/politics
|
||||
- name: huffpost
|
||||
homepage: https://www.huffpost.com
|
||||
feed: https://www.huffpost.com/section/politics/feed
|
||||
- name: mother-jones
|
||||
homepage: https://www.motherjones.com
|
||||
feed: https://www.motherjones.com/politics/feed/
|
||||
- name: the-nation
|
||||
homepage: https://www.thenation.com
|
||||
feed: https://www.thenation.com/feed/?post_type=article
|
||||
# WSJ's politics feed is dead; the working feed is general news (not the
|
||||
# Opinion feed — news and opinion are rated separately by every incumbent
|
||||
# rater, and we collect news). Topic-mix tradeoff noted in METHODOLOGY D9.
|
||||
- name: wsj
|
||||
homepage: https://www.wsj.com
|
||||
feed: https://feeds.a.dj.com/rss/RSSWorldNews.xml
|
||||
- name: national-review
|
||||
homepage: https://www.nationalreview.com
|
||||
feed: https://www.nationalreview.com/feed/
|
||||
- name: washington-examiner
|
||||
homepage: https://www.washingtonexaminer.com
|
||||
feed: https://www.washingtonexaminer.com/feed
|
||||
- name: ny-post
|
||||
homepage: https://nypost.com
|
||||
feed: https://nypost.com/politics/feed/
|
||||
- name: fox-news
|
||||
homepage: https://www.foxnews.com
|
||||
feed: https://moxie.foxnews.com/google-publisher/politics.xml
|
||||
- name: daily-wire
|
||||
homepage: https://www.dailywire.com
|
||||
feed: https://www.dailywire.com/feeds/rss.xml
|
||||
- name: breitbart
|
||||
homepage: https://www.breitbart.com
|
||||
feed: https://feeds.feedburner.com/breitbart
|
||||
- name: newsmax
|
||||
homepage: https://www.newsmax.com
|
||||
feed: https://www.newsmax.com/rss/Politics/1/
|
||||
@@ -0,0 +1,55 @@
|
||||
# ADR-0001: Story-selection ideal points, anchored to congressional language
|
||||
|
||||
- **Status**: accepted
|
||||
- **Date**: 2026-07-10
|
||||
- **Supersedes**: none
|
||||
|
||||
## Decision
|
||||
|
||||
tiltmeter v0.1 measures outlet political lean from **story selection**: outlets are
|
||||
placed on a latent axis by correspondence analysis of the outlet×story coverage
|
||||
matrix (ideal-point estimation — outlets as legislators, coverage decisions as
|
||||
votes). The axis is **oriented by an external political reference** — similarity of
|
||||
each pole to Democratic vs. Republican language in recent Congressional Record
|
||||
speeches — so no outlet's lean is assumed anywhere in the pipeline. Scores are
|
||||
per-window time series with bootstrap confidence intervals. No editorial or LLM
|
||||
judgment exists in the scoring path.
|
||||
|
||||
## Rationale (sources)
|
||||
|
||||
- Selection is a real, strong bias channel: D'Alessio & Allen 2000 (taxonomy);
|
||||
Budak, Goel & Rao 2016 (bias operates largely via issue selection); Rönnback,
|
||||
Emmery & Brighton 2025 (coverage features most informative at outlet level).
|
||||
- The estimation tradition is proven: Poole & Rosenthal 1985 (ideal points);
|
||||
Slapin & Proksch 2008 (unsupervised political scaling from text).
|
||||
- Anchoring to politicians keeps the reference frame factual and the validation
|
||||
non-circular: Gentzkow & Shapiro 2010; DW-NOMINATE (voteview.com).
|
||||
- No judge in the loop: LLMs measurably lean (Rozado 2024; Santurkar et al. 2023);
|
||||
human panels are the incumbents' admission of the same problem.
|
||||
|
||||
Full citations: [docs/research.md](../research.md) §6.
|
||||
|
||||
## Alternatives considered
|
||||
|
||||
1. **Declared anchor outlets** ("Mother Jones = left, Breitbart = right") — one
|
||||
editorial bit, but precisely the judgment this project exists to remove. Rejected
|
||||
after methodology review, 2026-07-10.
|
||||
2. **Language-similarity scoring as the primary signal** (Gentzkow–Shapiro style) —
|
||||
deferred to v0.2 (S2); requires the Gentzkow, Shapiro & Taddy 2019 estimator to
|
||||
avoid finite-sample bias. In v0.1 congressional language is used only to orient
|
||||
the axis sign.
|
||||
3. **Audience-based scoring** — platform data access is gated; dead end since ~2018.
|
||||
4. **LLM-as-judge** — unreproducible across model versions and lean-contaminated;
|
||||
rejected on principle (METHODOLOGY.md D8).
|
||||
5. **Human panels with published worksheets** — transparent but unscalable and
|
||||
governance-fragile; this is the incumbents' territory anyway.
|
||||
|
||||
## Failure modes / risks accepted
|
||||
|
||||
- The first principal axis may capture topic mix rather than politics; mitigated by
|
||||
politics-section feeds and published evidence pages; checked by the D7 validation
|
||||
gate (Spearman ρ ≥ 0.7 vs. AllSides and Ad Fontes orderings).
|
||||
- 20 outlets over a 2–4 week window may yield an unstable axis; bootstrap CIs make
|
||||
the instability visible rather than hidden.
|
||||
- The congressional reference frame defines "center" as the US two-party midpoint;
|
||||
drift or positions outside that frame are invisible. Stated in every release.
|
||||
@@ -0,0 +1,113 @@
|
||||
# Glossary
|
||||
|
||||
Plain definitions of every technical term used in this project's docs. If you
|
||||
hit a term anywhere in tiltmeter that isn't here, that's a bug — file it.
|
||||
|
||||
### RSS feed
|
||||
A machine-readable list an outlet publishes of its latest articles: headline,
|
||||
link, timestamp. It's how tiltmeter knows what each outlet published without
|
||||
scraping their whole site.
|
||||
|
||||
### Corpus
|
||||
The full collection of articles tiltmeter has gathered. Scores are always
|
||||
computed from a stated slice of it, never from "the web" loosely.
|
||||
|
||||
### Snapshot
|
||||
A frozen slice of the corpus: all articles from a stated time window, pinned
|
||||
by their fingerprints. Ratings are computed from snapshots so results can be
|
||||
checked and re-run later.
|
||||
|
||||
### Fingerprint (hash)
|
||||
A short code (SHA-256) computed from an article's exact text. Change one
|
||||
character of the article and the code changes completely. Fingerprints prove
|
||||
that a rating was computed from exactly these articles and no others.
|
||||
|
||||
### Manifest
|
||||
The published list of what's in a snapshot: each article's link, headline,
|
||||
outlet, timestamp, and fingerprint — but not its full text (which stays with
|
||||
the outlet). Anyone can re-download the articles and verify the fingerprints.
|
||||
|
||||
### Embedding
|
||||
A way of turning a piece of text into a list of numbers so that similar texts
|
||||
get similar numbers. tiltmeter uses embeddings only to spot when two articles
|
||||
cover the same story — never to judge them.
|
||||
|
||||
### Clustering
|
||||
Automatically grouping similar things. Here: grouping all outlets' articles
|
||||
about the same news event into one "story."
|
||||
|
||||
### Story cluster
|
||||
One news event and all the articles about it, across outlets. The unit of
|
||||
tiltmeter's analysis: for each story, which outlets covered it?
|
||||
|
||||
### Coverage matrix
|
||||
A big grid: one row per outlet, one column per story, marking who covered
|
||||
what. All of tiltmeter's scoring reads off this grid.
|
||||
|
||||
### Correspondence analysis
|
||||
A standard statistical method that turns a grid like the coverage matrix into
|
||||
a map: things with similar patterns end up near each other. The "seating
|
||||
chart" in the plain-language docs.
|
||||
|
||||
### Principal axis
|
||||
The single strongest direction of difference in that map — the line along
|
||||
which outlets' coverage choices differ most. tiltmeter checks whether that
|
||||
line matches political lean, rather than assuming it.
|
||||
|
||||
### Anchor
|
||||
The external reference that tells the map which end is politically left and
|
||||
which is right. tiltmeter's anchor is language from congressional speeches —
|
||||
public records, not anyone's opinion of an outlet.
|
||||
|
||||
### DW-NOMINATE
|
||||
A long-running political science measure that places members of Congress on
|
||||
a left–right scale using their actual voting records. Public data, published
|
||||
at voteview.com.
|
||||
|
||||
### Bootstrap
|
||||
A standard way to measure how sure we are of a number: recompute it many
|
||||
times on random re-samples of the data and watch how much it wobbles.
|
||||
|
||||
### Confidence interval
|
||||
The honest error range around a score, produced by the bootstrap. "−0.3 ± 0.1"
|
||||
means: don't read anything into differences smaller than that.
|
||||
|
||||
### Spearman rank correlation
|
||||
A statistic (written ρ, "rho") measuring how similarly two lists rank the same
|
||||
items, from −1 to +1. We use it to compare our outlet ordering with the
|
||||
commercial raters' ordering.
|
||||
|
||||
### Sensitivity analysis
|
||||
Re-running the pipeline with the judgment-call settings changed (time window,
|
||||
grouping threshold) and publishing how much the scores move. If a small
|
||||
setting change swings the results, that's disclosed, not hidden.
|
||||
|
||||
### Tunable
|
||||
A pipeline setting that is a practical judgment call rather than something the
|
||||
research literature dictates. Every tunable is labeled as such in
|
||||
METHODOLOGY.md and covered by the sensitivity analysis.
|
||||
|
||||
### ADR (decision record)
|
||||
A short numbered document recording one methodology decision: what was
|
||||
decided, why, what the alternatives were, and what could go wrong. Decisions
|
||||
are never edited in place — a new record supersedes an old one, so the
|
||||
method's history stays visible.
|
||||
|
||||
### SemVer (semantic versioning)
|
||||
Version numbers with meaning (MAJOR.MINOR.PATCH). Any change that could alter
|
||||
published ratings bumps the version, so every ratings file names the exact
|
||||
method that produced it.
|
||||
|
||||
### Reproducibility
|
||||
The property that anyone can re-run the pipeline on the same snapshot and get
|
||||
the same output, byte for byte. The project's core promise.
|
||||
|
||||
### Ideal-point estimation
|
||||
The political science technique of placing actors on a scale from their
|
||||
observed choices — classically, placing legislators by their votes. tiltmeter
|
||||
places outlets by their coverage choices: outlets are the legislators, stories
|
||||
are the votes.
|
||||
|
||||
### Lede
|
||||
The opening sentences of a news article, summarizing the story. tiltmeter
|
||||
embeds headline + lede to group articles into story clusters.
|
||||
@@ -0,0 +1,79 @@
|
||||
# How tiltmeter works
|
||||
|
||||
This page explains the whole system in plain language. No math. Every claim
|
||||
here links to the technical spec, [METHODOLOGY.md](../METHODOLOGY.md), which
|
||||
has the full details and sources. Words in *italics* are defined in the
|
||||
[glossary](glossary.md).
|
||||
|
||||
## The problem
|
||||
|
||||
Some news outlets lean left. Some lean right. Companies already sell ratings
|
||||
that say which is which. But those ratings come from panels of people voting
|
||||
behind closed doors. You cannot check their work. You just have to trust them.
|
||||
|
||||
tiltmeter is different. It is a measuring tool. Anyone can run it, check every
|
||||
step, and get the same numbers. If you think a rating is wrong, you can look
|
||||
at the exact news stories behind it and see for yourself.
|
||||
|
||||
## The idea: a seating chart for the news
|
||||
|
||||
Picture every big news event of the month as a party. Not every outlet shows
|
||||
up to every party. Each outlet picks which stories it covers. Those picks are
|
||||
choices, and choices reveal leanings.
|
||||
|
||||
tiltmeter watches those choices. Outlets that keep covering the same kinds of
|
||||
stories get seated near each other on a map. Outlets that make very different
|
||||
picks sit far apart. That map is built from the outlets' own behavior. Nobody
|
||||
votes on it. ([D3](../METHODOLOGY.md#d3-the-v1-signal-is-story-selection-framed-as-ideal-point-estimation))
|
||||
|
||||
One question is left: which side of the map is "left" and which is "right"?
|
||||
We do not decide that by opinion either. We compare the language of each side
|
||||
of the map to speeches from Congress. One pole will sound more like Democrats.
|
||||
The other will sound more like Republicans. Party membership is a public fact,
|
||||
not a rating. So the whole map is anchored to public records. ([D5](../METHODOLOGY.md#d5-the-axis-is-oriented-by-congressional-language-not-by-assumed-outlet-leans))
|
||||
|
||||
## The steps
|
||||
|
||||
1. **Collect.** Several times a day, tiltmeter reads the public article feeds
|
||||
of 20 US outlets, from far left to far right. It saves each new article
|
||||
with a *fingerprint* — a code that proves the text has not been changed.
|
||||
([D9](../METHODOLOGY.md#d9-the-corpus-is-politics-section-rss-stored-locally-published-as-manifests))
|
||||
2. **Group.** Articles about the same event are grouped into one story. Now we
|
||||
can see which outlets covered each story and which stayed silent.
|
||||
3. **Map.** The pattern of "covered it / skipped it" choices places each
|
||||
outlet on the seating chart.
|
||||
4. **Label the sides.** Speeches from Congress tell us which end of the chart
|
||||
is left and which is right.
|
||||
5. **Score.** Each outlet gets a number from −1 (left) to +1 (right), plus an
|
||||
honest error range. Lean can drift, so every score has a date. There are no
|
||||
permanent labels. ([D6](../METHODOLOGY.md#d6-scores-are-time-series-with-uncertainty-never-permanent-labels))
|
||||
|
||||
## How we check ourselves
|
||||
|
||||
We compare our numbers to the big commercial ratings. If our order of outlets
|
||||
is close to theirs, the method works. If not, we fix the method — in public,
|
||||
with a written record of what changed and why. One thing worth knowing: the
|
||||
commercial raters often disagree with *each other*. So matching them exactly
|
||||
is not even the goal. ([D7](../METHODOLOGY.md#d7-validation-against-incumbent-raters-is-a-gate-with-published-sensitivity))
|
||||
|
||||
We also publish "what if" checks. What if we group stories a bit differently?
|
||||
What if we use a different time window? If small choices like these swing the
|
||||
scores a lot, you deserve to know. ([D7](../METHODOLOGY.md#d7-validation-against-incumbent-raters-is-a-gate-with-published-sensitivity))
|
||||
|
||||
## What tiltmeter does not do
|
||||
|
||||
- It does not rate truth or quality. A score near zero does not mean "good".
|
||||
It means "picks stories like the middle of Congress talks". ([D2](../METHODOLOGY.md#d2-version-1-rates-political-lean-only))
|
||||
- It does not use AI models to judge the news. Studies show those models have
|
||||
leans of their own. ([D8](../METHODOLOGY.md#d8-no-llm-judgment-in-the-scoring-path))
|
||||
- It does not let anyone — including us — nudge a rating by hand. There is no
|
||||
hand. ([D1](../METHODOLOGY.md#d1-ratings-come-from-a-reproducible-pipeline-not-editorial-judgment))
|
||||
|
||||
## Check our work
|
||||
|
||||
Every published score links to an evidence page: the real headlines that drove
|
||||
it, the outlets that sat nearest, the error range, and the exact data batch
|
||||
used. And anyone can rerun the whole thing with three commands and get the
|
||||
same file, byte for byte. ([D10](../METHODOLOGY.md#d10-every-number-is-traceable-to-evidence-a-layman-can-read))
|
||||
|
||||
That is the whole point. Do not trust us. Check.
|
||||
@@ -0,0 +1,36 @@
|
||||
# Outlets collected
|
||||
|
||||
<!-- GENERATED FILE — do not edit by hand.
|
||||
Source: config/outlets.yaml
|
||||
Regenerate: uv run python scripts/gen_outlets_doc.py -->
|
||||
|
||||
The outlets tiltmeter currently collects from, straight from
|
||||
[`config/outlets.yaml`](../config/outlets.yaml). Selection criteria and the
|
||||
swap policy for dead feeds are documented in
|
||||
[METHODOLOGY.md, decision D4](../METHODOLOGY.md#d4-twenty-outlets-deliberately-spread-chosen-once-and-openly);
|
||||
individual swaps are recorded in the [CHANGELOG](../CHANGELOG.md).
|
||||
|
||||
| Outlet | Homepage | Feed |
|
||||
|---|---|---|
|
||||
| axios | <https://www.axios.com> | <https://api.axios.com/feed/> |
|
||||
| breitbart | <https://www.breitbart.com> | <https://feeds.feedburner.com/breitbart> |
|
||||
| cnn | <https://www.cnn.com> | <http://rss.cnn.com/rss/cnn_allpolitics.rss> |
|
||||
| cs-monitor | <https://www.csmonitor.com> | <https://rss.csmonitor.com/feeds/politics> |
|
||||
| daily-wire | <https://www.dailywire.com> | <https://www.dailywire.com/feeds/rss.xml> |
|
||||
| fox-news | <https://www.foxnews.com> | <https://moxie.foxnews.com/google-publisher/politics.xml> |
|
||||
| huffpost | <https://www.huffpost.com> | <https://www.huffpost.com/section/politics/feed> |
|
||||
| mother-jones | <https://www.motherjones.com> | <https://www.motherjones.com/politics/feed/> |
|
||||
| national-review | <https://www.nationalreview.com> | <https://www.nationalreview.com/feed/> |
|
||||
| nbc-news | <https://www.nbcnews.com> | <https://feeds.nbcnews.com/nbcnews/public/politics> |
|
||||
| newsmax | <https://www.newsmax.com> | <https://www.newsmax.com/rss/Politics/1/> |
|
||||
| npr | <https://www.npr.org> | <https://feeds.npr.org/1014/rss.xml> |
|
||||
| ny-post | <https://nypost.com> | <https://nypost.com/politics/feed/> |
|
||||
| nytimes | <https://www.nytimes.com> | <https://rss.nytimes.com/services/xml/rss/nyt/Politics.xml> |
|
||||
| pbs-newshour | <https://www.pbs.org/newshour> | <https://www.pbs.org/newshour/feeds/rss/politics> |
|
||||
| the-hill | <https://thehill.com> | <https://thehill.com/homenews/feed/> |
|
||||
| the-nation | <https://www.thenation.com> | <https://www.thenation.com/feed/?post_type=article> |
|
||||
| washington-examiner | <https://www.washingtonexaminer.com> | <https://www.washingtonexaminer.com/feed> |
|
||||
| washington-post | <https://www.washingtonpost.com> | <https://feeds.washingtonpost.com/rss/politics> |
|
||||
| wsj | <https://www.wsj.com> | <https://feeds.a.dj.com/rss/RSSWorldNews.xml> |
|
||||
|
||||
Total: 20 outlets.
|
||||
@@ -0,0 +1,158 @@
|
||||
# Research Foundations — Measuring Media Lean
|
||||
|
||||
tiltmeter descends from two research traditions: the **commercial rating industry**
|
||||
(AllSides, Ad Fontes, MBFC — structured human judgment behind closed data) and four
|
||||
decades of **academic measurement** of media bias (open methods, mostly one-off
|
||||
studies that never became living datasets). This document surveys both, extracts what
|
||||
is and is not measurable, and records the design implications. It informs
|
||||
[METHODOLOGY.md](../METHODOLOGY.md); where the two disagree, reconcile deliberately.
|
||||
|
||||
All venues verified 2026-07-10. Links in §6.
|
||||
|
||||
---
|
||||
|
||||
## 1. Lineage
|
||||
|
||||
### 1.a The rating industry: balanced panels, closed data
|
||||
|
||||
- **AllSides** (2012–) — Blind Bias Surveys™: cross-spectrum respondents rate content
|
||||
with branding stripped. Editorial reviews: multipartisan panels of ≥6 (or trios)
|
||||
from left/center/right deliberate to consensus. Output: 5-point scale plus a
|
||||
numeric meter. Community feedback as a secondary signal.
|
||||
- **Ad Fontes Media** (2017–) — trios of trained analysts (one self-identified left,
|
||||
center, right each; ≥20 hours training) score individual articles on an 8-part
|
||||
rubric (veracity, expression, headline/graphics, political position, language,
|
||||
comparison…). Article bias −42..+42, aggregated to outlet scores. Methodology
|
||||
white paper published 2021.
|
||||
- **Media Bias/Fact Check** (2015–) — small team applying published criteria
|
||||
(wording, sourcing, story choice, endorsements). Widest coverage, least rigorous;
|
||||
ironically the most-used ground truth in academic work because it covers the most
|
||||
sources.
|
||||
|
||||
The industry's shared premise: bias in the rater is unavoidable, so *balance the
|
||||
raters*. The panels are the method. None of it is reproducible — you cannot rerun a
|
||||
panel — and the per-rating evidence is not published. That gap, not the ratings
|
||||
themselves, is what tiltmeter fills: **remove the panel instead of balancing it.**
|
||||
|
||||
### 1.b The academic tradition: open methods, dead datasets
|
||||
|
||||
Economists and political scientists have measured outlet lean reproducibly since the
|
||||
1980s, but almost every study was a one-off: a method, a paper, a static replication
|
||||
archive. Nothing compounds. The methods, however, are exactly what a living open
|
||||
dataset needs (see §2).
|
||||
|
||||
A partial exception and the closest living relative: **Penn's Media Bias Detector**
|
||||
(CHI 2025) — LLM-annotated articles aggregated into dynamic per-publisher views,
|
||||
deliberately refusing static outlet labels. It trades reproducibility for coverage by
|
||||
putting an LLM in the judgment loop (see METHODOLOGY.md D8 for why we refuse that
|
||||
trade).
|
||||
|
||||
### 1.c Why re-run the experiment now
|
||||
|
||||
Three things changed. Sentence embeddings made cross-outlet story clustering cheap
|
||||
and good — the expensive part of selection-bias measurement is now a commodity.
|
||||
Congressional text and ideology data (govinfo.gov bulk data, voteview.com) are
|
||||
freely downloadable, so the classic anchoring strategy no longer requires
|
||||
institutional data access. And the incumbent raters have demonstrated durable demand
|
||||
for outlet-lean ratings while leaving methodology transparency entirely unserved.
|
||||
|
||||
## 2. Measurement traditions
|
||||
|
||||
The canonical survey — **Puglisi & Snyder, "Empirical Studies of Media Bias"
|
||||
(Handbook of Media Economics 1B, 2015)** — divides implicit-bias measures into three
|
||||
families. They map onto tiltmeter's roadmap directly:
|
||||
|
||||
| Family (Puglisi & Snyder) | Canonical works | tiltmeter |
|
||||
|---|---|---|
|
||||
| Intensity of coverage (what gets covered, how much) | D'Alessio & Allen 2000 (gatekeeping); Larcinese, Puglisi & Snyder 2011; Budak, Goel & Rao 2016 | **S1, v0.1** — story-selection ideal points |
|
||||
| Comparison with other political actors | Groseclose & Milyo 2005 (contested); **Gentzkow & Shapiro 2010**; Gentzkow, Shapiro & Taddy 2019 | **S2, v0.2** — congressional-language similarity; v0.1 uses it for axis orientation only |
|
||||
| Tone | sentiment/valence studies; BABE dataset (EMNLP Findings 2021) for sentence-level bias | future work |
|
||||
|
||||
Supporting traditions:
|
||||
|
||||
- **Ideal-point estimation** (Poole & Rosenthal 1985 → DW-NOMINATE): place actors on
|
||||
a latent dimension from their observed choices. tiltmeter's outlet×story
|
||||
correspondence analysis is roll-call analysis where outlets are legislators and
|
||||
coverage decisions are votes. Ho & Quinn (2008) already placed outlets by ideal
|
||||
points, using editorial positions.
|
||||
- **Unsupervised text scaling** (Slapin & Proksch 2008, "Wordfish"; supervised
|
||||
variant: Laver, Benoit & Garry 2003, "Wordscores"): latent political dimensions
|
||||
recovered from text without labeled outlets. S1's closest methodological cousin.
|
||||
- **Audience-based scores** (Bakshy, Messing & Adamic 2015, Science; Barberá 2015):
|
||||
infer outlet lean from who consumes/shares it. Methodologically attractive,
|
||||
practically dead — platform data access is gated. Included here because its logic
|
||||
(lean is relational, anchored in an external population) survives in our
|
||||
congressional anchoring.
|
||||
- **Theory of selection** — why S1 measures something real: White 1950
|
||||
(gatekeeping), Galtung & Ruge 1965 (news values), McCombs & Shaw 1972
|
||||
(agenda-setting: media tell you what to think *about*), Entman 1993 (framing).
|
||||
Political economy — why outlets lean at all: Mullainathan & Shleifer 2005 (AER,
|
||||
demand-side); Gentzkow & Shapiro 2006 (JPE, reputation); DellaVigna & Kaplan 2007
|
||||
(QJE, the Fox News effect — why any of this matters).
|
||||
|
||||
## 3. What is and is not measurable
|
||||
|
||||
Lessons the literature forces on us:
|
||||
|
||||
1. **Lean is relational.** Every credible measure anchors to an external population
|
||||
with independently known politics — Congress (roll-call votes), voters, audiences.
|
||||
A "view from nowhere" rating is an unstated anchor, not a neutral one.
|
||||
2. **The method chosen shapes the structure found.** MBFC labels vs. audience-derived
|
||||
labels agree only 46%; two human-annotated label sources agree 57% (Rönnback,
|
||||
Emmery & Brighton 2025, PLOS One). Measurement choice is itself a finding — which
|
||||
is the argument for publishing the method, not just the numbers.
|
||||
3. **Selection carries signal.** Budak, Goel & Rao (2016) found outlet bias operates
|
||||
largely through issue selection rather than explicit slant; Rönnback et al. (2025)
|
||||
found coverage features the most informative outlet-level predictors. The cheapest
|
||||
honest signal is *what outlets choose to cover*.
|
||||
4. **Judges have lean too.** LLMs measurably lean (Rozado 2024, PLOS One; Santurkar
|
||||
et al. 2023, ICML); so do human panels — the industry's entire architecture is an
|
||||
admission of that. Any judgment component relocates the bias problem.
|
||||
5. **Perfect agreement with any one rater would be a bug.** Given 46–57% inter-rater
|
||||
agreement, a method that matched AllSides at ρ ≈ 1 would have learned AllSides,
|
||||
not lean.
|
||||
|
||||
## 4. Design implications
|
||||
|
||||
| Lesson (§3) | Design response (METHODOLOGY.md) |
|
||||
|---|---|
|
||||
| Lean is relational | D5: explicit anchor — congressional party discourse; frame stated in every release |
|
||||
| Method shapes findings | D1: open reproducible pipeline; D7: published sensitivity sweeps |
|
||||
| Selection carries signal | D3: story-selection ideal points as the v1 signal |
|
||||
| Judges have lean | D1: no editorial judgment; D8: no LLM judgment |
|
||||
| Perfect agreement = overfit | D7: validation gate at ρ ≥ 0.7, not ρ → 1 |
|
||||
| Raters disagree; lean drifts | D6: time series with confidence intervals, never static labels |
|
||||
| One-off studies don't compound | Corpus manifests + versioned releases: a living dataset others can extend |
|
||||
|
||||
## 5. Reading list (in argument order)
|
||||
|
||||
1. Puglisi & Snyder 2015 — the map of the field; read first.
|
||||
2. D'Alessio & Allen 2000 — the bias taxonomy (gatekeeping / coverage / statement).
|
||||
3. McCombs & Shaw 1972 — agenda-setting; the theoretical charter for selection bias.
|
||||
4. Poole & Rosenthal 1985 — ideal points; the estimation tradition S1 sits in.
|
||||
5. Slapin & Proksch 2008 — unsupervised political scaling from text.
|
||||
6. Gentzkow & Shapiro 2010 — congressional-language anchoring; our D5 and future S2.
|
||||
7. Gentzkow, Shapiro & Taddy 2019 — read before implementing S2; the naive estimator
|
||||
is severely biased in finite samples.
|
||||
8. Budak, Goel & Rao 2016 — selection is where the bias lives.
|
||||
9. Grimmer & Stewart 2013 — validation discipline for all of the above.
|
||||
10. Rönnback, Emmery & Brighton 2025 — the modern end-to-end attempt; ground-truth
|
||||
disagreement numbers.
|
||||
11. Rozado 2024; Santurkar et al. 2023 — why no LLM judges.
|
||||
12. Spinde et al., ACM Computing Surveys 2023 — the NLP field map (3,140 papers).
|
||||
|
||||
## 6. Sources
|
||||
|
||||
- Puglisi & Snyder 2015: <https://www.sciencedirect.com/science/article/abs/pii/B9780444636850000152>
|
||||
- Gentzkow & Shapiro 2010: <https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA7195>
|
||||
- Gentzkow, Shapiro & Taddy 2019: <https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA16566>
|
||||
- Budak, Goel & Rao 2016: <https://doi.org/10.1093/poq/nfw007>
|
||||
- Rönnback, Emmery & Brighton 2025: <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321418>
|
||||
- Media Bias Detector (CHI 2025): <https://dl.acm.org/doi/10.1145/3706598.3713716>
|
||||
- Rozado 2024: <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306621>
|
||||
- Spinde et al. 2023 (ACM CS): <https://arxiv.org/abs/2312.16148>
|
||||
- BABE (Findings of EMNLP 2021): <https://aclanthology.org/2021.findings-emnlp.101/>
|
||||
- AllSides methodology: <https://www.allsides.com/about/media-bias-rating-methods>
|
||||
- Ad Fontes white paper: <https://adfontesmedia.com/white-paper-2021/>
|
||||
- DW-NOMINATE / voteview: <https://voteview.com>
|
||||
- Congressional Record bulk data: <https://www.govinfo.gov/bulkdata>
|
||||
@@ -0,0 +1,37 @@
|
||||
[project]
|
||||
name = "tiltmeter"
|
||||
version = "0.1.0"
|
||||
description = "Auditable, reproducible political-lean ratings for news outlets"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
license = { text = "MIT" }
|
||||
authors = [{ name = "flan", email = "flan@arch.fyi" }]
|
||||
dependencies = [
|
||||
"feedparser>=6.0",
|
||||
"trafilatura>=1.12",
|
||||
"pyyaml>=6.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
tiltmeter = "tiltmeter.cli:main"
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"pytest>=8.0",
|
||||
"ruff>=0.8",
|
||||
"textstat>=0.7",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["src/tiltmeter"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
target-version = "py313"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
Executable
+25
@@ -0,0 +1,25 @@
|
||||
#!/usr/bin/env bash
|
||||
# Docs move with the code, enforced: if pipeline code or config changed in a
|
||||
# commit range but no documentation did, fail. Used by CI (ci.yml) and usable
|
||||
# locally: scripts/check_docs_sync.sh <base>..<head>
|
||||
set -euo pipefail
|
||||
|
||||
range="${1:?usage: check_docs_sync.sh <base>..<head>}"
|
||||
|
||||
changed="$(git diff --name-only "$range")"
|
||||
|
||||
code_changed="$(echo "$changed" | grep -E '^(src/|config/)' || true)"
|
||||
docs_changed="$(echo "$changed" | grep -E '^(docs/|METHODOLOGY\.md|CHANGELOG\.md|README\.md)' || true)"
|
||||
|
||||
if [[ -n "$code_changed" && -z "$docs_changed" ]]; then
|
||||
echo "FAIL: code/config changed without any docs change in $range"
|
||||
echo
|
||||
echo "Changed code/config files:"
|
||||
echo "$code_changed" | sed 's/^/ /'
|
||||
echo
|
||||
echo "Update the relevant docs (METHODOLOGY.md decision block, CHANGELOG.md"
|
||||
echo "entry, docs/*) in the same change. Docs are part of the product here."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "docs-sync ok"
|
||||
@@ -0,0 +1,56 @@
|
||||
"""Keep docs/outlets.md automatically in sync with config/outlets.yaml.
|
||||
|
||||
The outlet table in the docs is generated, never hand-edited: this script
|
||||
renders it from the config, and tests/test_docs.py fails CI whenever the
|
||||
committed file differs from what the config would generate. To update docs
|
||||
after changing outlets: uv run python scripts/gen_outlets_doc.py
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
CONFIG = ROOT / "config" / "outlets.yaml"
|
||||
TARGET = ROOT / "docs" / "outlets.md"
|
||||
|
||||
HEADER = """\
|
||||
# Outlets collected
|
||||
|
||||
<!-- GENERATED FILE — do not edit by hand.
|
||||
Source: config/outlets.yaml
|
||||
Regenerate: uv run python scripts/gen_outlets_doc.py -->
|
||||
|
||||
The outlets tiltmeter currently collects from, straight from
|
||||
[`config/outlets.yaml`](../config/outlets.yaml). Selection criteria and the
|
||||
swap policy for dead feeds are documented in
|
||||
[METHODOLOGY.md, decision D4](../METHODOLOGY.md#d4-twenty-outlets-deliberately-spread-chosen-once-and-openly);
|
||||
individual swaps are recorded in the [CHANGELOG](../CHANGELOG.md).
|
||||
|
||||
| Outlet | Homepage | Feed |
|
||||
|---|---|---|
|
||||
"""
|
||||
|
||||
|
||||
def render() -> str:
|
||||
with open(CONFIG) as f:
|
||||
outlets = yaml.safe_load(f)["outlets"]
|
||||
rows = [
|
||||
f"| {o['name']} | <{o['homepage']}> | <{o['feed']}> |"
|
||||
for o in sorted(outlets, key=lambda o: o["name"])
|
||||
]
|
||||
return HEADER + "\n".join(rows) + f"\n\nTotal: {len(outlets)} outlets.\n"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
content = render()
|
||||
if "--check" in sys.argv:
|
||||
current = TARGET.read_text() if TARGET.exists() else ""
|
||||
if current != content:
|
||||
print("docs/outlets.md is stale; run: uv run python scripts/gen_outlets_doc.py")
|
||||
sys.exit(1)
|
||||
print("docs/outlets.md is in sync")
|
||||
else:
|
||||
TARGET.write_text(content)
|
||||
print(f"wrote {TARGET}")
|
||||
@@ -0,0 +1,7 @@
|
||||
"""tiltmeter: auditable, reproducible political-lean ratings for news outlets.
|
||||
|
||||
Every module in this package opens with a plain-language docstring stating the
|
||||
question it answers. See docs/how-it-works.md for the full plain-language tour.
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
@@ -0,0 +1,66 @@
|
||||
"""How does a person drive this thing?
|
||||
|
||||
The command-line interface. Two commands exist so far:
|
||||
|
||||
tiltmeter ingest — poll every outlet's feed once and store new articles
|
||||
tiltmeter status — show how many articles we hold per outlet
|
||||
|
||||
Later milestones add: snapshot, run, validate, report.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
|
||||
from tiltmeter import db, ingest
|
||||
|
||||
DEFAULT_CONFIG = "config/outlets.yaml"
|
||||
DEFAULT_DB = "data/tiltmeter.db"
|
||||
|
||||
|
||||
def cmd_ingest(args: argparse.Namespace) -> int:
|
||||
results = ingest.ingest_all(args.config, args.db, fetch_text=not args.no_text)
|
||||
failed = [r for r in results if "error" in r]
|
||||
for r in results:
|
||||
if "error" in r:
|
||||
print(f" FAIL {r['outlet']}: {r['error']}")
|
||||
else:
|
||||
print(f" ok {r['outlet']}: {r['seen']} in feed, {r['new']} new")
|
||||
return 1 if len(failed) == len(results) and results else 0
|
||||
|
||||
|
||||
def cmd_status(args: argparse.Namespace) -> int:
|
||||
conn = db.connect(args.db)
|
||||
rows = db.outlet_counts(conn)
|
||||
if not rows:
|
||||
print("no articles collected yet")
|
||||
return 0
|
||||
width = max(len(o) for o, _ in rows)
|
||||
for outlet, count in rows:
|
||||
print(f" {outlet:<{width}} {count}")
|
||||
print(f" {'TOTAL':<{width}} {sum(c for _, c in rows)}")
|
||||
return 0
|
||||
|
||||
|
||||
def main(argv: list[str] | None = None) -> int:
|
||||
parser = argparse.ArgumentParser(prog="tiltmeter", description=__doc__)
|
||||
parser.add_argument("--db", default=DEFAULT_DB, help="path to the article database")
|
||||
sub = parser.add_subparsers(dest="command", required=True)
|
||||
|
||||
p_ingest = sub.add_parser("ingest", help="poll all outlet feeds once")
|
||||
p_ingest.add_argument("--config", default=DEFAULT_CONFIG)
|
||||
p_ingest.add_argument(
|
||||
"--no-text", action="store_true", help="skip full-text fetch (feed metadata only)"
|
||||
)
|
||||
p_ingest.set_defaults(func=cmd_ingest)
|
||||
|
||||
p_status = sub.add_parser("status", help="article counts per outlet")
|
||||
p_status.set_defaults(func=cmd_status)
|
||||
|
||||
args = parser.parse_args(argv)
|
||||
logging.basicConfig(level=logging.INFO, format="%(levelname)s %(name)s: %(message)s")
|
||||
return args.func(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,78 @@
|
||||
"""Where do collected articles live?
|
||||
|
||||
One SQLite file holds every article we have ever collected: which outlet
|
||||
published it, when we saw it, its headline, its text, and a fingerprint (hash)
|
||||
of its content. The fingerprint is what later lets anyone verify that a
|
||||
published rating was computed from exactly these articles and no others.
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS articles (
|
||||
id INTEGER PRIMARY KEY,
|
||||
outlet TEXT NOT NULL,
|
||||
url TEXT NOT NULL UNIQUE,
|
||||
title TEXT NOT NULL,
|
||||
published TEXT,
|
||||
fetched_at TEXT NOT NULL,
|
||||
summary TEXT,
|
||||
text TEXT,
|
||||
content_hash TEXT NOT NULL
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_articles_outlet ON articles (outlet);
|
||||
CREATE INDEX IF NOT EXISTS idx_articles_fetched ON articles (fetched_at);
|
||||
"""
|
||||
|
||||
|
||||
def connect(db_path: str | Path) -> sqlite3.Connection:
|
||||
"""Open (creating if needed) the article database."""
|
||||
path = Path(db_path)
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
conn = sqlite3.connect(path)
|
||||
conn.executescript(SCHEMA)
|
||||
return conn
|
||||
|
||||
|
||||
def content_hash(title: str, text: str) -> str:
|
||||
"""Fingerprint an article's content.
|
||||
|
||||
SHA-256 over headline + body. Two articles with the same fingerprint have
|
||||
identical content; a snapshot manifest of fingerprints pins a corpus.
|
||||
"""
|
||||
return hashlib.sha256((title + "\x1f" + text).encode("utf-8")).hexdigest()
|
||||
|
||||
|
||||
def have_url(conn: sqlite3.Connection, url: str) -> bool:
|
||||
"""Have we already collected this article? (Dedup key is the URL.)"""
|
||||
row = conn.execute("SELECT 1 FROM articles WHERE url = ? LIMIT 1", (url,)).fetchone()
|
||||
return row is not None
|
||||
|
||||
|
||||
def insert_article(
|
||||
conn: sqlite3.Connection,
|
||||
*,
|
||||
outlet: str,
|
||||
url: str,
|
||||
title: str,
|
||||
published: str | None,
|
||||
fetched_at: str,
|
||||
summary: str | None,
|
||||
text: str | None,
|
||||
) -> None:
|
||||
"""Store one newly collected article."""
|
||||
conn.execute(
|
||||
"INSERT OR IGNORE INTO articles"
|
||||
" (outlet, url, title, published, fetched_at, summary, text, content_hash)"
|
||||
" VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
(outlet, url, title, published, fetched_at, summary, text, content_hash(title, text or "")),
|
||||
)
|
||||
|
||||
|
||||
def outlet_counts(conn: sqlite3.Connection) -> list[tuple[str, int]]:
|
||||
"""How many articles do we have per outlet? (The health check for ingestion.)"""
|
||||
return conn.execute(
|
||||
"SELECT outlet, COUNT(*) FROM articles GROUP BY outlet ORDER BY outlet"
|
||||
).fetchall()
|
||||
@@ -0,0 +1,93 @@
|
||||
"""Which articles did each outlet publish today?
|
||||
|
||||
This module polls each outlet's RSS feed (a machine-readable list of its
|
||||
latest articles), fetches the full text of any article we haven't seen
|
||||
before, and stores it. It never rates anything — it only collects, so that
|
||||
every later step works from the same recorded evidence.
|
||||
|
||||
Politeness rules: we identify ourselves with an honest User-Agent, fetch each
|
||||
article at most once ever (URLs are deduplicated), and pause between fetches.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
|
||||
import feedparser
|
||||
import trafilatura
|
||||
import yaml
|
||||
|
||||
from tiltmeter import db
|
||||
|
||||
log = logging.getLogger("tiltmeter.ingest")
|
||||
|
||||
USER_AGENT = "tiltmeter/0.1 (+https://github.com/sudolulo/tiltmeter; research corpus builder)"
|
||||
# Floor chosen to stay under WAF/rate-limit radar on a single domain; a block
|
||||
# would cost us corpus coverage, which is worth more than ingest speed.
|
||||
FETCH_DELAY_SECONDS = 0.2
|
||||
|
||||
|
||||
def load_outlets(config_path: str) -> list[dict]:
|
||||
"""Read the outlet list (name, feed URL) from config/outlets.yaml."""
|
||||
with open(config_path) as f:
|
||||
cfg = yaml.safe_load(f)
|
||||
return cfg["outlets"]
|
||||
|
||||
|
||||
def fetch_article_text(url: str) -> str | None:
|
||||
"""Download one article page and extract its readable text."""
|
||||
html = trafilatura.fetch_url(url)
|
||||
if html is None:
|
||||
return None
|
||||
return trafilatura.extract(html, include_comments=False, include_tables=False)
|
||||
|
||||
|
||||
def ingest_outlet(conn, outlet: dict, *, fetch_text: bool = True) -> tuple[int, int]:
|
||||
"""Poll one outlet's feed; store unseen articles. Returns (seen, new)."""
|
||||
parsed = feedparser.parse(outlet["feed"], agent=USER_AGENT)
|
||||
now = datetime.now(timezone.utc).isoformat()
|
||||
seen, new = 0, 0
|
||||
for entry in parsed.entries:
|
||||
url = entry.get("link")
|
||||
title = (entry.get("title") or "").strip()
|
||||
if not url or not title:
|
||||
continue
|
||||
seen += 1
|
||||
if db.have_url(conn, url):
|
||||
continue
|
||||
text = None
|
||||
if fetch_text:
|
||||
try:
|
||||
text = fetch_article_text(url)
|
||||
except Exception as exc: # noqa: BLE001 - one bad page must not stop the run
|
||||
log.warning("text fetch failed for %s: %s", url, exc)
|
||||
time.sleep(FETCH_DELAY_SECONDS)
|
||||
db.insert_article(
|
||||
conn,
|
||||
outlet=outlet["name"],
|
||||
url=url,
|
||||
title=title,
|
||||
published=entry.get("published") or entry.get("updated"),
|
||||
fetched_at=now,
|
||||
summary=(entry.get("summary") or "").strip() or None,
|
||||
text=text,
|
||||
)
|
||||
new += 1
|
||||
conn.commit()
|
||||
return seen, new
|
||||
|
||||
|
||||
def ingest_all(config_path: str, db_path: str, *, fetch_text: bool = True) -> list[dict]:
|
||||
"""Poll every configured outlet once. Returns a per-outlet result report."""
|
||||
conn = db.connect(db_path)
|
||||
results = []
|
||||
for outlet in load_outlets(config_path):
|
||||
try:
|
||||
seen, new = ingest_outlet(conn, outlet, fetch_text=fetch_text)
|
||||
results.append({"outlet": outlet["name"], "seen": seen, "new": new})
|
||||
log.info("%s: %d entries in feed, %d new", outlet["name"], seen, new)
|
||||
except Exception as exc: # noqa: BLE001 - one bad feed must not stop the run
|
||||
results.append({"outlet": outlet["name"], "error": str(exc)})
|
||||
log.error("%s: feed failed: %s", outlet["name"], exc)
|
||||
conn.close()
|
||||
return results
|
||||
@@ -0,0 +1,151 @@
|
||||
"""Do the docs keep the promises the README makes?
|
||||
|
||||
These tests are the enforcement arm of the auditability contract:
|
||||
- the plain-language walkthrough stays at or below a grade-10 reading level
|
||||
- every required jargon term has a glossary entry, and no glossary entry is dead
|
||||
- every METHODOLOGY decision block carries its four mandatory fields
|
||||
- decision records (ADRs) are sequential and complete
|
||||
- the generated outlets table matches the config it is generated from
|
||||
"""
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import textstat
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
HOW_IT_WORKS = ROOT / "docs" / "how-it-works.md"
|
||||
GLOSSARY = ROOT / "docs" / "glossary.md"
|
||||
METHODOLOGY = ROOT / "METHODOLOGY.md"
|
||||
DECISIONS = ROOT / "docs" / "decisions"
|
||||
|
||||
MAX_GRADE = 10.0
|
||||
|
||||
|
||||
def strip_markdown(text: str) -> str:
|
||||
text = re.sub(r"\]\([^)]*\)", "]", text) # link targets are not prose
|
||||
text = re.sub(r"<https?://[^>]*>", "", text)
|
||||
return re.sub(r"[#*`>\[\]]", "", text)
|
||||
|
||||
|
||||
def test_how_it_works_readability():
|
||||
grade = textstat.flesch_kincaid_grade(strip_markdown(HOW_IT_WORKS.read_text()))
|
||||
assert grade <= MAX_GRADE, (
|
||||
f"docs/how-it-works.md reads at grade {grade:.1f}, above the promised "
|
||||
f"grade-{MAX_GRADE:.0f} ceiling. Shorten sentences, use simpler words."
|
||||
)
|
||||
|
||||
|
||||
# Jargon that project docs are allowed to use only because the glossary defines it.
|
||||
# Add a term here whenever a new one enters the docs.
|
||||
REQUIRED_GLOSSARY_TERMS = [
|
||||
"RSS feed",
|
||||
"Corpus",
|
||||
"Snapshot",
|
||||
"Fingerprint (hash)",
|
||||
"Manifest",
|
||||
"Embedding",
|
||||
"Clustering",
|
||||
"Story cluster",
|
||||
"Coverage matrix",
|
||||
"Correspondence analysis",
|
||||
"Principal axis",
|
||||
"Anchor",
|
||||
"DW-NOMINATE",
|
||||
"Bootstrap",
|
||||
"Confidence interval",
|
||||
"Spearman rank correlation",
|
||||
"Sensitivity analysis",
|
||||
"Tunable",
|
||||
"ADR (decision record)",
|
||||
"SemVer (semantic versioning)",
|
||||
"Reproducibility",
|
||||
"Ideal-point estimation",
|
||||
"Lede",
|
||||
]
|
||||
|
||||
|
||||
def glossary_terms() -> list[str]:
|
||||
return re.findall(r"^### (.+)$", GLOSSARY.read_text(), flags=re.MULTILINE)
|
||||
|
||||
|
||||
def test_glossary_covers_required_terms():
|
||||
missing = set(REQUIRED_GLOSSARY_TERMS) - set(glossary_terms())
|
||||
assert not missing, f"glossary is missing required terms: {sorted(missing)}"
|
||||
|
||||
|
||||
def test_glossary_has_no_dead_entries():
|
||||
"""Every glossary entry must actually be used somewhere in the docs."""
|
||||
docs_text = " ".join(
|
||||
p.read_text().lower()
|
||||
for p in [
|
||||
HOW_IT_WORKS,
|
||||
METHODOLOGY,
|
||||
ROOT / "README.md",
|
||||
ROOT / "CHANGELOG.md",
|
||||
ROOT / "docs" / "research.md",
|
||||
]
|
||||
)
|
||||
dead = []
|
||||
for term in glossary_terms():
|
||||
# match on the head word(s) before any parenthetical, e.g. "Fingerprint (hash)"
|
||||
head = re.sub(r"\s*\(.*\)$", "", term).lower()
|
||||
stem = head[:-1] if head.endswith("y") else head # reproducibility/reproducible
|
||||
if stem not in docs_text:
|
||||
dead.append(term)
|
||||
assert not dead, f"glossary entries never used in any doc: {dead}"
|
||||
|
||||
|
||||
DECISION_FIELDS = ["**Decision**", "**Rationale**", "**Alternatives considered**",
|
||||
"**Failure modes**"]
|
||||
|
||||
|
||||
def test_methodology_decision_blocks_complete():
|
||||
text = METHODOLOGY.read_text()
|
||||
blocks = re.split(r"^## D(\d+)\. ", text, flags=re.MULTILINE)
|
||||
assert len(blocks) > 1, "METHODOLOGY.md contains no decision blocks"
|
||||
numbers = [int(n) for n in blocks[1::2]]
|
||||
assert numbers == list(range(1, len(numbers) + 1)), (
|
||||
f"decision blocks must be numbered sequentially from D1, got {numbers}"
|
||||
)
|
||||
for number, body in zip(blocks[1::2], blocks[2::2]):
|
||||
missing = [f for f in DECISION_FIELDS if f not in body]
|
||||
assert not missing, f"METHODOLOGY.md D{number} is missing fields: {missing}"
|
||||
|
||||
|
||||
ADR_SECTIONS = ["## Decision", "## Rationale", "## Alternatives considered", "## Failure modes"]
|
||||
ADR_METADATA = ["**Status**", "**Date**", "**Supersedes**"]
|
||||
|
||||
|
||||
def test_adrs_sequential_and_complete():
|
||||
adrs = sorted(DECISIONS.glob("*.md"))
|
||||
assert adrs, "docs/decisions/ contains no decision records"
|
||||
numbers = []
|
||||
for path in adrs:
|
||||
m = re.match(r"^(\d{4})-[a-z0-9-]+\.md$", path.name)
|
||||
assert m, f"ADR filename must be NNNN-kebab-title.md, got {path.name}"
|
||||
numbers.append(int(m.group(1)))
|
||||
text = path.read_text()
|
||||
for section in ADR_SECTIONS:
|
||||
assert re.search(f"^{re.escape(section)}", text, flags=re.MULTILINE), (
|
||||
f"{path.name} is missing a section starting with '{section}'"
|
||||
)
|
||||
for field in ADR_METADATA:
|
||||
assert field in text, f"{path.name} is missing metadata field {field}"
|
||||
assert numbers == list(range(1, len(numbers) + 1)), (
|
||||
f"ADRs must be numbered sequentially from 0001, got {numbers}"
|
||||
)
|
||||
|
||||
|
||||
def test_outlets_doc_in_sync_with_config():
|
||||
import importlib.util
|
||||
|
||||
spec = importlib.util.spec_from_file_location(
|
||||
"gen_outlets_doc", ROOT / "scripts" / "gen_outlets_doc.py"
|
||||
)
|
||||
mod = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(mod)
|
||||
committed = (ROOT / "docs" / "outlets.md").read_text()
|
||||
assert committed == mod.render(), (
|
||||
"docs/outlets.md is stale. Regenerate: uv run python scripts/gen_outlets_doc.py"
|
||||
)
|
||||
@@ -0,0 +1,51 @@
|
||||
"""Does the collector store, deduplicate, and fingerprint articles correctly?
|
||||
|
||||
Pure unit tests — no network. Feed parsing and article fetching are exercised
|
||||
by the live cron run; what must never regress silently is the storage
|
||||
contract: one row per URL, stable fingerprints, accurate per-outlet counts.
|
||||
"""
|
||||
|
||||
from tiltmeter import db
|
||||
|
||||
|
||||
def test_insert_and_dedup_by_url():
|
||||
conn = db.connect(":memory:")
|
||||
kwargs = dict(
|
||||
outlet="example",
|
||||
url="https://example.com/a",
|
||||
title="Headline",
|
||||
published=None,
|
||||
fetched_at="2026-07-10T00:00:00+00:00",
|
||||
summary=None,
|
||||
text="Body text.",
|
||||
)
|
||||
db.insert_article(conn, **kwargs)
|
||||
db.insert_article(conn, **kwargs) # same URL again: must not duplicate
|
||||
assert db.outlet_counts(conn) == [("example", 1)]
|
||||
assert db.have_url(conn, "https://example.com/a")
|
||||
assert not db.have_url(conn, "https://example.com/b")
|
||||
|
||||
|
||||
def test_content_hash_is_stable_and_content_sensitive():
|
||||
a = db.content_hash("Headline", "Body text.")
|
||||
assert a == db.content_hash("Headline", "Body text.") # deterministic
|
||||
assert a != db.content_hash("Headline", "Body text!") # any edit changes it
|
||||
assert a != db.content_hash("Headline2", "Body text.")
|
||||
# title/text boundary is unambiguous: moving a word across it changes the hash
|
||||
assert db.content_hash("A B", "C") != db.content_hash("A", "B C")
|
||||
|
||||
|
||||
def test_counts_group_by_outlet():
|
||||
conn = db.connect(":memory:")
|
||||
for i, outlet in enumerate(["left-times", "right-post", "left-times"]):
|
||||
db.insert_article(
|
||||
conn,
|
||||
outlet=outlet,
|
||||
url=f"https://example.com/{i}",
|
||||
title=f"T{i}",
|
||||
published=None,
|
||||
fetched_at="2026-07-10T00:00:00+00:00",
|
||||
summary=None,
|
||||
text="x",
|
||||
)
|
||||
assert db.outlet_counts(conn) == [("left-times", 2), ("right-post", 1)]
|
||||
@@ -0,0 +1,625 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.13"
|
||||
|
||||
[[package]]
|
||||
name = "babel"
|
||||
version = "2.18.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2026.6.17"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c9/c7/424b75da314c1045981bd9777432fad05a9e0c69daa4ed7e308bbaffe405/certifi-2026.6.17.tar.gz", hash = "sha256:024c88eeec92ca068db80f02b8b07c9cef7b9fe261d1d535abfd5abd6f6af432", size = 134594, upload-time = "2026-06-17T10:31:07.894Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ef/2f/c5464532e965badff2f4c4c1a3a83f5697f0d7c407ed0cda44aaa99bb451/certifi-2026.6.17-py3-none-any.whl", hash = "sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db", size = 133289, upload-time = "2026-06-17T10:31:06.348Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.4.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/bd/2a/23f34ec9d04624958e137efdc394888716353190e75f25dd22c7a2c7a8aa/charset_normalizer-3.4.9.tar.gz", hash = "sha256:673611bbd43f0810bec0b0f028ddeaaa501190339cac411f347ac76917c3ae7b", size = 152439, upload-time = "2026-07-07T14:34:58.454Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b2/06/97ec2aeae780b31d742b6352218b43841a6871e2564578ca522dce4a45c3/charset_normalizer-3.4.9-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:440eede837960000d74978f0eba527be106b5b9aee0daf779d395276ed0b0614", size = 317688, upload-time = "2026-07-07T14:33:35.408Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d0/39/8ff066c672434225f8d25f8b739f992af250944392173dcc88362681c9bf/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:21e764fd1e70b6a3e205a0e46f3051701f98a8cb3fad66eeb80e48bb502f8698", size = 214982, upload-time = "2026-07-07T14:33:36.996Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/92/8f/3a47a3667c83c2df9483d91644c6c107de3bf8874aa1793da9d3012eb986/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e4fd89cc178bced6ad29cb3e6dd4aa63fa5017c3524dbd0b25998fb64a87cc8b", size = 236460, upload-time = "2026-07-07T14:33:38.536Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f1/60/b22cdbee7e4013dab8b0d7647fc6181120fbbbc8f7025c226d15bd5a47fc/charset_normalizer-3.4.9-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:bd47ba7fc3ca94896759ea0109775132d3e7ab921fbf54038e1bab2e46c313c9", size = 232003, upload-time = "2026-07-07T14:33:40.059Z" },
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{ url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321, upload-time = "2026-04-24T15:22:05.876Z" },
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]
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||||
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||||
[[package]]
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||||
name = "tzlocal"
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version = "5.4.4"
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||||
source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "tzdata", marker = "sys_platform == 'win32'" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/81/5b/879b2f932adfa7a053c360d50bc896c977fa6426109185f7c12ebdd0cb9d/tzlocal-5.4.4.tar.gz", hash = "sha256:8dbb8660838688a7b6ba4fed31d18dedf842afb4d47ca050d6d891c2c15f3be4", size = 31170, upload-time = "2026-06-29T08:03:40.026Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/9e/a4/017a7a6cbe387d961a688ec31364ae60a5c4e22c96ae9921b79a947c855d/tzlocal-5.4.4-py3-none-any.whl", hash = "sha256:aae09f0126a8a86fa736be266eb4a471380d26a0de3bc14844e7821fee3e2a15", size = 18115, upload-time = "2026-06-29T08:03:38.666Z" },
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]
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[[package]]
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name = "urllib3"
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version = "2.7.0"
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source = { registry = "https://pypi.org/simple" }
|
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sdist = { url = "https://files.pythonhosted.org/packages/53/0c/06f8b233b8fd13b9e5ee11424ef85419ba0d8ba0b3138bf360be2ff56953/urllib3-2.7.0.tar.gz", hash = "sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c", size = 433602, upload-time = "2026-05-07T16:13:18.596Z" }
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wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/7f/3e/5db95bcf282c52709639744ca2a8b149baccf648e39c8cc87553df9eae0c/urllib3-2.7.0-py3-none-any.whl", hash = "sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897", size = 131087, upload-time = "2026-05-07T16:13:17.151Z" },
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||||
]
|
||||
Reference in New Issue
Block a user