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+# Part 3b dataset-selection audit
+
+**Status:** HALT FOR OWNER REVIEW — dataset selected and analysis pre-registered;
+no adapter or outcome run is authorized by this report.
+
+**Repository base:** `3dd8726514ba5c01145b3cd83612e937e4657f34`
+(`main`, after Part 3a)  
+**Audit date:** 2026-07-18  
+**Evidence tier:** selection logic and the frozen protocol are reviewable in the
+repository; any later real-data result will be Tier 2, reproducible by source
+revision and checksum but not run in CI.
+
+## Verdict
+
+None of the eight named candidates clears all four hard gates. The recurring
+blockers are not data quality: most have no explicit dataset license permitting
+the proposed local analysis and aggregate publication, and several lack
+authentic, replicated cohorts.
+
+The audit found one additional candidate that does clear all four gates:
+**UC Berkeley's Measuring Hate Speech (MHS) dataset**. It is the single
+recommendation for a bounded first study of political-ideology cohorts. The
+primary analysis would cover the 77 comments that, under the frozen structural
+filter below, have at least two eligible conservative and two eligible liberal
+annotators. That is a useful pilot, not a measurement of universal or
+"production" prevalence.
+
+No Part-3b outcome metric was computed. In particular, this audit did not
+compute fork rates, manufactured-consensus rates, geometry gaps, reliability
+spreads, or cohort-conditioned label rates. While inspecting the MHS schema, a
+category-enumeration query incidentally returned global label counts. Those
+counts are deliberately omitted and were not used to choose the dataset,
+contrast, filter, or metrics.
+
+## Method: four hard gates
+
+A candidate is rejected if any gate fails or remains unresolved.
+
+1. **Authentic cohorts.** Cohort membership must be supplied before this study
+   by the dataset authors or collection design. Post-hoc clustering on labels,
+   explanations, or embeddings is disqualifying. A single annotator with a
+   demographic profile is not a replicated cohort.
+2. **Repeated identities.** Stable annotator identifiers must support
+   leave-one-out reliability. The declared floor is **20 judgments per
+   included annotator**; the floor is not lowered after seeing outcomes.
+3. **Compatible license.** An explicit dataset license or terms must permit
+   local analysis and publication of aggregate results and a non-row-level
+   reproducibility manifest. A paper saying data are "available," a public
+   download link, or a license on the paper does not substitute for a dataset
+   license. A failure here means *clearance was not demonstrated*, not that use
+   is necessarily forbidden.
+4. **Pre-registered comparison.** The cohort contrast and metrics must be
+   declarable from collection metadata before examining cohort-conditioned
+   outcomes. The precise protocol for the selected dataset is frozen below.
+
+The evidence review used papers, official dataset pages, and author repositories
+at the pinned revisions listed in the source ledger. Repository-level license
+checks were made at those revisions rather than inferred from GitHub visibility.
+
+## Gate results
+
+| Candidate | G1 authentic cohorts | G2 repeated IDs, floor 20 | G3 explicit compatible license | G4 prospective contrast | Decision |
+|---|---|---|---|---|---|
+| POPQUORN | PASS — author-recorded demographics | PASS — repeated workers, 50 items each | FAIL — no dataset license found at audited release | PASS — an author-defined demographic contrast can be frozen prospectively | REJECT |
+| VariErr NLI | FAIL — four annotators, no demographic or designed cohorts | PASS — the same four annotate 500 items | FAIL — no dataset license found | FAIL — no authentic cohort contrast | REJECT |
+| HS-Brexit | PASS — designed target/control groups | PASS — six annotators label all 1,120 items | FAIL — neither the paper nor audited LeWiDi release supplies a dataset license | PASS — target versus control is pre-existing | REJECT |
+| MD-Agreement | FAIL — worker IDs, but no authentic annotator cohorts | UNRESOLVED — IDs repeat, but the audited sources do not establish a qualifying floor-20 subset | FAIL — audited release has no dataset license | FAIL — item domains are not annotator cohorts | REJECT |
+| ConvAbuse | FAIL — expert panel, but no pre-existing subcohorts | PASS — stable expert identifiers with repeated judgments | PASS — dataset repository carries CC BY 4.0 | FAIL — splitting experts after collection would be post hoc | REJECT |
+| ArMIS | FAIL — three full-corpus profiles are three individuals, not replicated cohorts | PASS — each of the three labels 964 items; the 32-person extension is below the floor | FAIL — availability language is not a dataset license | FAIL — a singleton comparison would confound person and cohort | REJECT |
+| Kumar et al. 2021 | PASS — author-recorded demographics and attitudes | PASS — each participant rates 20 items | FAIL — official data page provides access but no dataset-use license | PASS — an author-defined demographic contrast can be frozen prospectively | REJECT |
+| Sap et al. 2022 | PASS — designed race/ideology strata | FAIL — the documented designs do not establish a floor-20 qualifying subset | FAIL — data are contact-gated and no dataset license is stated | PASS — designed strata support a prospective contrast | REJECT |
+| **Measuring Hate Speech** | **PASS — author-recorded political ideology** | **PASS — 2,316 annotators meet the frozen floor** | **PASS — CC BY 4.0** | **PASS — exact conservative/liberal contrast frozen below** | **SELECT** |
+
+## Candidate evidence
+
+### POPQUORN
+
+The [POPQUORN paper](https://aclanthology.org/2023.law-1.25/) reports
+authentic demographic attributes and repeated workers: the offensiveness task
+has 262 participants, 13,036 annotations, and 50 comments presented to each
+participant. This is a strong cohort-and-power fit. However, the
+[audited author repository](https://github.com/Jiaxin-Pei/Potato-Prolific-Dataset/tree/dbb118c29b3d9bcce5aa172d8249ded9ece4df84)
+contains no dataset license. Release and availability statements do not clear
+Gate 3.
+
+### VariErr NLI
+
+The [VariErr NLI paper](https://aclanthology.org/2024.acl-long.123/) and
+[author repository](https://github.com/mainlp/VariErr-NLI/tree/b2cbdd3bc3ca2a618fc16ac078f471623152c923)
+document four stable annotators who label and explain all 500 items. The reasons
+make it an attractive companion for variation-versus-error research, but the
+four people are not author-defined cohorts. The audited release also has no
+dataset license. It cannot support the proposed prevalence study without
+inventing cohorts.
+
+### HS-Brexit
+
+The [HS-Brexit paper](https://arxiv.org/abs/2106.15896) has the cleanest
+designed two-group structure among the named candidates: three target-group and
+three control-group annotators label the same 1,120 tweets. The official
+[LeWiDi task page](https://codalab.lisn.upsaclay.fr/competitions/6146) and
+[audited LeWiDi repository](https://github.com/Le-Wi-Di/le-wi-di.github.io/tree/45d7b06e4c36b59472d6bdf9281f7f1e395eb0e0)
+make the files accessible, but neither supplies an explicit dataset-use
+license. Public access alone does not clear Gate 3.
+
+### MD-Agreement
+
+The [MD-Agreement paper](https://aclanthology.org/2021.emnlp-main.822/)
+describes 10,753 tweets with five crowd judgments each and hundreds of workers.
+The release preserves worker identifiers, but provides neither authentic
+annotator cohorts nor evidence in the audited sources that identifies a
+specific floor-20 subset. Item domains are not a substitute for annotator
+cohorts. The LeWiDi release is also unlicensed.
+
+### ConvAbuse
+
+The [ConvAbuse paper](https://aclanthology.org/2021.emnlp-main.587/) uses a
+small panel of expert gender-studies annotators with stable identities. Its
+[official repository](https://github.com/amandacurry/convabuse/tree/c0a9469f48956e868276c605d499010ea3c7c0d0)
+has an explicit [CC BY 4.0 license](https://github.com/amandacurry/convabuse/blob/c0a9469f48956e868276c605d499010ea3c7c0d0/LICENSE),
+so licensing is not the blocker. The experts are not divided into authentic,
+replicated subcohorts. Constructing groups from their observed labels would
+violate the anti-circularity gate.
+
+### ArMIS
+
+The [ArMIS paper](https://aclanthology.org/2022.lrec-1.244/) records profiles
+for three annotators who each label the full 964-item corpus: a liberal woman,
+a moderate woman, and a conservative man. Those are three confounded
+individuals, not replicated cohorts; a group contrast would be indistinguishable
+from an individual-annotator contrast. A further 32 annotators label only 11
+items, below the frozen reliability floor. The paper's availability language
+does not provide a dataset license.
+
+### Kumar et al. 2021
+
+[Kumar et al.'s study](https://www.usenix.org/system/files/soups2021-kumar.pdf)
+collects 20 ratings from each of 17,280 participants and records detailed
+demographics and beliefs. It is methodologically promising and exactly meets
+the repeated-judgment floor. The
+[official data page](https://data.esrg.stanford.edu/study/toxicity-perspectives)
+provides a contact-mediated encrypted download but states no dataset license or
+terms that clear the proposed use. The report therefore does not infer
+permission from accessibility.
+
+### Sap et al. 2022
+
+The [Sap et al. paper](https://aclanthology.org/2022.naacl-main.431/) uses
+authentic race and ideology strata. Its breadth-of-workers study gives 641
+participants 15 posts each; its breadth-of-posts study has 173 participants and
+3,171 ratings, averaging 18.3 per participant with varying participation. The
+paper does not establish a qualifying floor-20 subset, and tells readers to
+contact the authors for anonymized data without stating a dataset license. It
+therefore fails Gates 2 and 3 even though its collection design avoids
+post-hoc cohorts.
+
+### UC Berkeley Measuring Hate Speech (selected)
+
+The [MHS paper](https://aclanthology.org/2022.nlperspectives-1.11/),
+[project site](https://hatespeech.berkeley.edu/), and
+[official dataset card](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech)
+document stable annotator identifiers, author-recorded demographic and
+political attributes, repeated ordinal hate-speech judgments, and a
+**CC BY 4.0** dataset license. The
+[license terms](https://creativecommons.org/licenses/by/4.0/) permit sharing
+and adaptation with attribution, satisfying the local-analysis and aggregate
+reporting use contemplated here.
+
+Structural checks were performed against official revision
+[`5468f6e`](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech/tree/5468f6e):
+
+- source file: `measuring-hate-speech.parquet`
+- SHA-256: `6819525ce61bc24344df9fc3f7bf48270b31038273cc27c67fc225b51433b0e1`
+- source size: 14,123,673 bytes
+- 135,556 annotation rows, 39,565 comments, 7,912 annotators
+- 2,316 annotators have at least 20 judgments; their observed judgment counts
+  range from 20 to 26 (median 21)
+- under the ideology assignment frozen below, the eligible population contains
+  662 conservative, 1,215 liberal, and 439 excluded neutral/no-opinion/null
+  annotators
+- 77 comments have at least two eligible conservative and two eligible liberal
+  judgments
+
+These are source-structure and eligibility counts, not outcome measurements.
+No licensed rows or annotator/comment identifiers are committed to this
+repository.
+
+## Scoring the gate-passer
+
+Only hard-gate passers are scored. Because MHS is the only passer, the scoring
+does not manufacture a ranking among rejected datasets.
+
+| Criterion | Range | MHS | Reason |
+|---|---:|---:|---|
+| Reasons/rationales | 0–4 | 2 | Ten ordinal dimensions provide richer context, but there are no free-text reasons |
+| Statistical power | 0–3 | 2 | Large corpus and repeated raters, but only 77 items clear the primary two-sided cohort filter |
+| Domain fit | 0–3 | 3 | Subjective hate-speech judgments directly exercise contested-label and manufactured-consensus instruments |
+| Cohort granularity | 0–3 | 2 | Authentic, sizeable ideology cohorts; within-item cohort coverage is sparse |
+| Operational tractability | 0–2 | 2 | Explicit license, fixed downloadable artifact, row-level stable IDs and categorical fields |
+| **Total** | **0–15** | **11** | **Best and only hard-gate passer** |
+
+The ten ordinal dimensions are not annotator reasons. Consequently,
+variation-versus-error attribution is outside the primary claim set.
+
+## Frozen prospective protocol
+
+This protocol is fixed before any cohort-conditioned outcome is calculated.
+Changing it requires a new dated pre-registration and an explanation made
+before rerunning the study.
+
+### Source and integrity
+
+- Dataset: UC Berkeley Measuring Hate Speech, official Hugging Face revision
+  `5468f6e`.
+- Input artifact SHA-256:
+  `6819525ce61bc24344df9fc3f7bf48270b31038273cc27c67fc225b51433b0e1`.
+- No source rows are committed. A later adapter/run may commit code, aggregate
+  results, and a manifest of hashes and counts only.
+- If the source revision or checksum changes, freeze the new revision and hash,
+  repeat the structural gate, and obtain owner approval before outcomes.
+
+### Eligibility and cohorts
+
+1. Include only annotators with at least **20 total non-null `hatespeech`
+   judgments** in the fixed source artifact.
+2. Assign **Conservative** only when the author-supplied political-ideology
+   value is exactly `extremely_conservative`, `conservative`, or
+   `slightly_conservative`.
+3. Assign **Liberal** only when it is exactly `extremely_liberal`, `liberal`,
+   or `slightly_liberal`.
+4. Exclude neutral, no-opinion, and null ideology values. Do not infer or impute
+   cohort membership.
+5. The primary item set contains comments with at least two eligible
+   Conservative and at least two eligible Liberal judgments. If a fresh
+   integrity-equivalent structural pass yields fewer than **50** primary items,
+   halt and return to the owner rather than weakening the filter.
+6. Preserve raw ordinal `hatespeech` values `{0, 1, 2}` as categorical strings.
+   Do not binarize, substitute an IRT score, or filter items using labels.
+
+### Primary metrics
+
+1. **Value-fork rate.** Use the current `disagreement.py` rule: each cohort
+   must have a unique plurality and the two cohort pluralities must differ.
+   Denominator: all primary items. A tied cohort has no cohort opinion and does
+   not create a fork.
+2. **Manufactured-consensus rate.** Use the current
+   `manufactured_consensus` field produced by `disagreement.py` over all primary
+   items, without changing its thresholds.
+3. **Geometry gap.** For each primary item, form the two cohort label
+   distributions and compute total variation between their arithmetic and
+   normalized geometric centres using `geometry.py`. Report the share with
+   disjoint support/undefined geometric centre separately. For defined values,
+   report median and interquartile range; do not replace undefined cases with a
+   maximum distance.
+4. **Reliability spread.** Use the current CONFIDENT-only, leave-one-out
+   reliability definition over the full converted corpus for eligible
+   annotators. Report only annotators with at least 20 cells actually scored as
+   CONFIDENT, including cohort coverage, cohort medians and interquartile
+   ranges, and the difference in cohort medians. If either cohort has fewer
+   than 30 qualifying annotators, label this comparison underpowered and
+   non-applicable; do not lower the floor.
+
+### Uncertainty and multiplicity
+
+- Report Wilson 95% intervals for fork and manufactured-consensus rates.
+- Use no null-hypothesis p-values in the initial study.
+- If an interval is reported for a median gap or cohort median difference, use
+  an item-level bootstrap with 10,000 resamples and fixed seed `20260718`.
+- Do not add gender, race, age, or intersections, and do not analyze the other
+  ordinal dimensions in the initial run. Any secondary contrast requires its
+  own prospective protocol. This excludes silent subgroup search and
+  result-dependent cohort selection.
+
+## Permitted claims and non-claims
+
+If the frozen study is approved and successfully run, it may report only:
+
+- dataset-specific, topology-qualified descriptive rates for the pinned MHS
+  revision and the exact Conservative/Liberal contrast above;
+- reproducible aggregate results tied to the source checksum, tool commit,
+  protocol, and run manifest; and
+- operational observations about whether the repository's instruments execute
+  on this bounded empirical case.
+
+It must not claim:
+
+- universal, population, or "production" prevalence of value forks or
+  manufactured consensus;
+- a causal effect of political ideology;
+- representation of every political group, platform, population, or time;
+- that any label is the correct ground truth;
+- validation of the repository's mathematical results by one dataset;
+- variation-versus-error attribution without annotator reasons; or
+- generalization beyond the pinned corpus and topology-qualified subset.
+
+The README's current statement that the repository ships the instruments rather
+than the empirical measurement remains correct and should not change now. No
+measurement has been run. If the owner later approves and publishes the study,
+retain that hedge and add an adjacent link to a separately labelled Tier-2
+appendix: one bounded dataset-specific measurement, not general production
+prevalence.
+
+## Decision requested; work stops here
+
+Owner review is required on exactly three points:
+
+1. approve or reject MHS as the Part-3b dataset;
+2. approve or revise the frozen Conservative/Liberal contrast and primary
+   metrics *before* any outcome run; and
+3. accept the Tier-2 evidence boundary and the non-claims above.
+
+Until that approval, do not build an MHS adapter, compute outcome metrics,
+change the README, or publish prevalence numbers. This report intentionally
+halts at dataset selection and pre-registration.
+
+## Source snapshot
+
+| Source | Audited locator |
+|---|---|
+| POPQUORN | [paper](https://aclanthology.org/2023.law-1.25/); [repository commit `dbb118c`](https://github.com/Jiaxin-Pei/Potato-Prolific-Dataset/tree/dbb118c29b3d9bcce5aa172d8249ded9ece4df84) |
+| VariErr NLI | [paper](https://aclanthology.org/2024.acl-long.123/); [repository commit `b2cbdd3`](https://github.com/mainlp/VariErr-NLI/tree/b2cbdd3bc3ca2a618fc16ac078f471623152c923) |
+| HS-Brexit / LeWiDi | [paper](https://arxiv.org/abs/2106.15896); [task](https://codalab.lisn.upsaclay.fr/competitions/6146); [repository commit `45d7b06`](https://github.com/Le-Wi-Di/le-wi-di.github.io/tree/45d7b06e4c36b59472d6bdf9281f7f1e395eb0e0) |
+| MD-Agreement | [paper](https://aclanthology.org/2021.emnlp-main.822/) |
+| ConvAbuse | [paper](https://aclanthology.org/2021.emnlp-main.587/); [repository commit `c0a9469`](https://github.com/amandacurry/convabuse/tree/c0a9469f48956e868276c605d499010ea3c7c0d0); [license](https://github.com/amandacurry/convabuse/blob/c0a9469f48956e868276c605d499010ea3c7c0d0/LICENSE) |
+| ArMIS | [paper](https://aclanthology.org/2022.lrec-1.244/) |
+| Kumar et al. | [paper](https://www.usenix.org/system/files/soups2021-kumar.pdf); [official data page](https://data.esrg.stanford.edu/study/toxicity-perspectives) |
+| Sap et al. | [paper](https://aclanthology.org/2022.naacl-main.431/) |
+| Measuring Hate Speech | [paper](https://aclanthology.org/2022.nlperspectives-1.11/); [project](https://hatespeech.berkeley.edu/); [dataset card](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech); [revision `5468f6e`](https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech/tree/5468f6e); [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |