LOCUS v1.1 with dimension scores

README.md

@@ -1,178 +1,192 @@
 ---
+pretty_name: "LOCUS v1.1"
 language:
 - en
-license: cc-by-nc-4.0
+license: "cc-by-nc-4.0"
 size_categories:
-- 1M<n<10M
+- "1M<n<10M"
 task_categories:
 - text-classification
-pretty_name: LOCUS v1.0
 tags:
 - law
-- legal-nlp
-- local-government
 - municipal-law
-- ordinances
-- classification
+- model-annotations
 configs:
-- config_name: default
+- config_name: sections
+  default: true
   data_files:
   - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: header
-    dtype: string
-  - name: content
-    dtype: string
-  - name: is_substantive
-    dtype: bool
-  - name: function
-    dtype: string
-  - name: topic
-    dtype: string
-  - name: source_jurisdiction_type
-    dtype: string
-  - name: state
-    dtype: string
-  - name: city
-    dtype: string
-  - name: county
-    dtype: string
-  - name: enforcement_discretion
-    dtype: float64
-  - name: opacity
-    dtype: float64
-  - name: paternalism
-    dtype: float64
-  - name: problem_salience
-    dtype: float64
-  splits:
-  - name: train
-    num_examples: 2211516
-rai:dataLimitations: '- Coverage is not exhaustive. LOCUS v1.0 does not represent
-  every U.S. municipal or county jurisdiction, and jurisdictions vary in how completely
-  their local law is digitized and codified.
-
-  - The `function` and `topic` labels are model-assigned and have not been fully human-validated.
-  Label noise should be expected, particularly at taxonomy boundaries (e.g., Rules
-  vs. Process, Buildings vs. Zoning).
-
-  - The topic taxonomy is intentionally coarse (Buildings, Business, Nuisance, Zoning,
-  Other) and does not capture fine-grained subject matter important for some downstream
-  tasks.
-
-  - Local law changes over time. The dataset reflects a snapshot and may not match
-  the current law of any given jurisdiction.
-
-  '
-rai:dataBiases: '- Geographic and population skew: larger and more urbanized jurisdictions,
-  and jurisdictions that publish their codes through major codification vendors, are
-  likely overrepresented relative to small or rural municipalities.
-
-  - State-level legal regimes differ, so the distribution of functions and topics
-  is not uniform across states; aggregate label frequencies should not be assumed
-  to generalize to any individual jurisdiction.
-
-  - The taxonomy itself encodes choices about what counts as "substantive" law (Rules
-  and Enforcement) versus procedural, contextual, or structural provisions. Alternative
-  legal-theoretic framings would produce different labels.
-
-  - Labels were produced by automated classifiers and may reflect biases of those
-  classifiers, including biases inherited from their training data.
-
-  - English-language only. Non-English text occasionally present in U.S. local codes
-  is not handled as a separate class.
-
-  '
-rai:personalSensitiveInformation: 'LOCUS v1.0 is built from publicly enacted municipal
-  and county law, which is a matter of public record. However, local ordinances can
-  incidentally contain references to identifiable individuals (e.g., named officials,
-  sponsors, or parties to specific proceedings) and to specific properties or businesses
-  (e.g., addresses in zoning provisions, license holders, named establishments). No
-  effort has been made to redact such incidental references. The dataset does not
-  contain non-public personal data, biometric data, health data, financial account
-  information, or government-issued identifiers.
-
-  '
-rai:dataUseCases: 'Intended uses include legal text classification research, analysis
-  of local-law structure and composition, substantive vs. non-substantive filtering
-  for downstream legal NLP pipelines, refinement of legal taxonomies, and weakly supervised
-  or human-in-the-loop annotation workflows. The dataset is not appropriate as legal
-  advice, as a substitute for human legal review, as a complete census of U.S. local
-  law, or as a fully validated benchmark in the absence of additional human auditing.
-
-  '
-rai:dataSocialImpact: 'Anticipated positive impacts include lowering the cost of empirical
-  research on local government law, supporting tools that help residents, journalists,
-  and researchers understand municipal regulation, and enabling reproducible study
-  of how local jurisdictions structure their codes. Anticipated risks include over-reliance
-  on automated classifications in settings with legal consequences, misinterpretation
-  of aggregated label frequencies as authoritative statements about the law, and downstream
-  tools surfacing model outputs to end users as if they were vetted legal information.
-  Users deploying systems built on this dataset should clearly disclose the automated
-  and unaudited nature of the labels and should not present outputs as legal advice.
-
-  '
-rai:hasSyntheticData: false
+    path: sections/*.parquet
+- config_name: labels
+  data_files:
+  - split: train
+    path: labels/*.parquet
+- config_name: human_audit
+  data_files:
+  - split: train
+    path: human_audit/*.parquet
+- config_name: counties
+  data_files:
+  - split: train
+    path: counties/*.parquet
 ---
 
-# LOCUS v1.0
+# LOCUS v1.1
+
+LOCUS (Local Ordinance Corpus for the United States) contains municipal and county legal text harmonized to county geography. This release includes three models' annotations and a human audit for primary function, regulated topic where applicable, and rubric confidence.
+
+[Paper](https://arxiv.org/abs/2606.19334) · [Code](https://github.com/transpAIrency/LOCUS)
 
-This repository contains the dataset presented in the paper [Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States](https://huggingface.co/papers/2606.19334).
+Annotations use Gemini (`gemini-3.8-flash`), OpenAI (`gpt-6-luna`, low reasoning), and Jev (`jev-1.14.0`).
 
-## Dataset Summary
-LOCUS v1.0 is a chunk-level dataset of U.S. municipal and county law text labeled by legal function. Each eligible chunk is assigned a `function`, a binary `is_substantive` label, and all substantive provisions are assigned a `topic`.
-The dataset is intended for legal text research, local-law structure analysis, substantive filtering, and downstream taxonomy refinement.
+## Subsets and coverage
 
-## Dataset Structure
-The unit of analysis is a text chunk derived from local law documents.
+| Subset | Rows | Contents |
+|---|---:|---|
+| `sections` | 3,073,770 | Default: identifiers, jurisdiction, text, consensus, all model labels, and dimension scores (29 columns). |
+| `labels` | 3,073,770 | The section identifiers and label columns, without jurisdiction or legal text (17 columns). |
+| `human_audit` | 250 | Human consensus, independent eligibility flags, and the three models' labels. |
+| `counties` | 3,144 | The county harmonization universe, including counties without a selected document. |
 
-## Scale
-Approximate scope for v1: `2,211,516` ordinances
+The sections contain **2,360 distinct documents**. The county table contains **3,144 counties**: **2,410 represented** (76.7%) and **734 without a selected document**. These are counts of the released county selection, not a claim that every ordinance in a represented county is included.
 
-## Label Schema
+The `sections` and `labels` rows share the same order, sorted by state, document_id, and section_index. The composite key `(document_id, section_index)` is unique. Parquet files use Zstandard compression.
 
-### Function
-Allowed values:
-- `Context`
-- `Rules`
-- `Process`
-- `Enforcement`
+## Loading
 
-### Substantive Indicator
-The released dataset enforces the following deterministic rule:
-- `is_substantive = 1` for `Rules` and `Enforcement`
-- `is_substantive = 0` for `Context`, `Process`, and `Structural`
+```python
+from datasets import load_dataset
 
-### Topic
-Used only when `is_substantive = 1`.
+repo_id = "LocalLaws/LOCUS-v1"
+sections = load_dataset(repo_id, "sections", split="train", streaming=True)
+labels = load_dataset(repo_id, "labels", split="train", streaming=True)
+human_audit = load_dataset(repo_id, "human_audit", split="train")
+counties = load_dataset(repo_id, "counties", split="train")
+```
 
-Allowed values:
-- `Buildings`
-- `Business`
-- `Nuisance`
-- `Zoning`
-- `Other`
+Streaming avoids downloading and materializing the entire sections table before iteration.
 
-## Recommended Uses
-LOCUS v1.0 is appropriate for:
-- legal text classification research
-- local law structure analysis
-- substantive versus non-substantive filtering
-- downstream taxonomy refinement
-- weakly supervised or human-in-the-loop legal NLP workflows
+## Column glossary
 
-## Out-of-Scope Uses
-LOCUS v1.0 should not be treated as:
-- legal advice
-- a substitute for human legal review
-- a complete census of all U.S. local law
-- a fully human-validated benchmark without additional auditing
+### sections
 
-## Citation
+| Column | Type | Nullable | Meaning |
+|---|---|---|---|
+| `document_id` | `string` | no | Stable source document identifier; join key with section_index. |
+| `section_index` | `int64` | no | Original index within its document; gaps and an initial zero are allowed. |
+| `state` | `string` | no | Uppercase two-letter jurisdiction state abbreviation. |
+| `jurisdiction_name` | `string` | no | Selected jurisdiction name from county harmonization. |
+| `jurisdiction_type` | `string` | no | city, county, city-county, or other; city-county combines municipal/county governments or functions, and other is Washington, DC. |
+| `jurisdiction_geoid` | `string` | yes | Single 5-digit county, 7-digit place, or 10-digit county-subdivision GEOID; null when several apply. Leading zeros are retained. |
+| `represented_county_fips` | `list<string>` | no | Nonempty list of represented county FIPS codes sorted by FIPS. |
+| `represented_county_name` | `list<string>` | no | County names aligned with represented_county_fips. |
+| `header` | `string` | yes | Source header with leading Markdown # marks and surrounding whitespace removed. |
+| `content` | `string` | no | Unchanged source section content. |
+| `function_majority` | `string` | yes | Function receiving at least two matching model votes; otherwise null. |
+| `function_votes` | `int8` | no | Largest number of matching function votes, from 0 to 3. |
+| `topic_majority` | `string` | yes | Topic receiving at least two matching votes; null also represents a not-applicable majority. |
+| `topic_votes` | `int8` | no | Largest number of matching topic votes, including not applicable, from 0 to 3. |
+| `gemini_function` | `string` | yes | Gemini primary function; null for an unavailable answer. |
+| `gemini_topic` | `string` | yes | Gemini topic (the codebook subcategory); null when inapplicable or unavailable. |
+| `gemini_confidence` | `string` | yes | Gemini rubric confidence: Low, Medium, or High; null when unavailable. |
+| `openai_function` | `string` | yes | OpenAI primary function; null for an unavailable answer. |
+| `openai_topic` | `string` | yes | OpenAI topic (the codebook subcategory); null when inapplicable or unavailable. |
+| `openai_confidence` | `string` | yes | OpenAI rubric confidence: Low, Medium, or High; null when unavailable. |
+| `jev_function` | `string` | yes | Jev primary function; null for an unavailable answer. |
+| `jev_topic` | `string` | yes | Jev topic (the codebook subcategory); null when inapplicable or unavailable. |
+| `jev_confidence` | `string` | yes | Jev rubric confidence: Low, Medium, or High; null when unavailable. |
+| `jev_function_probs` | `struct<float32>` | yes | Jev scores keyed by Context, Rules, Process, Enforcement, Structural; null iff jev_function is null. |
+| `jev_topic_probs` | `struct<float32>` | yes | Jev scores keyed by Buildings, Business, Zoning, Nuisance, Other; null iff jev_topic is null. |
+| `enforcement_discretion` | `float64` | yes | Enforcement discretion score, standardized over non-Structural sections; null iff function_majority is Structural. |
+| `opacity` | `float64` | yes | Opacity score, standardized over non-Structural sections; null iff function_majority is Structural. |
+| `paternalism` | `float64` | yes | Paternalism score, standardized over non-Structural sections; null iff function_majority is Structural. |
+| `problem_salience` | `float64` | yes | Problem salience score, standardized over non-Structural sections; null iff function_majority is Structural. |
+
+### labels
+
+`document_id`, `section_index`, the four consensus columns, the nine per-model label columns, and the two Jev probability columns have the same definitions and types as `sections`.
+
+### human_audit
+
+| Column | Type | Meaning |
+|---|---|---|
+| `audit_id` | string | Stable audit excerpt identifier. |
+| `text` | string | Original human-audit excerpt text. |
+| `human_function`, `human_topic`, `human_confidence` | nullable string | Human majority consensus, recased to the release vocabularies. |
+| `human_function_eligible`, `human_topic_eligible`, `human_confidence_eligible` | bool | Independent flags identifying fields eligible for evaluation. |
+| `human_annotations` | int64 | Number of human annotations for the excerpt. |
+| `gemini_*`, `openai_*`, `jev_*`, `jev_*_probs` | as in sections | Model labels and scores joined by audit_id. |
+
+### counties
+
+| Column | Type | Meaning |
+|---|---|---|
+| `county_fips` | string | Five-digit county FIPS code, preserving leading zeros. |
+| `county_name`, `state` | string | County name and uppercase state abbreviation from harmonization. |
+| `document_id` | nullable string | Selected representative document; null for an uncovered county. |
+| `jurisdiction_name`, `jurisdiction_type` | nullable string | The selected jurisdiction, with the same definitions as sections. |
+| `selection_reason` | string | Recorded harmonization selection reason. |
+| `section_count` | nullable int64 | Released sections in the selected document; null when none is selected. |
+
+Pre-cleaning page counts are excluded because they do not describe the released text.
+
+## Labels, consensus, and missing values
+
+`topic` is the annotation codebook's `subcategory`. Function values are `Context`, `Rules`, `Process`, `Enforcement`, and `Structural`. Topic values are `Buildings`, `Business`, `Zoning`, `Nuisance`, and `Other`. Confidence values are `Low`, `Medium`, and `High`.
+
+Unavailable or unsuccessful model answers use real null values for all of that model's labels. A null topic with a non-null function means topic does not apply (`Context`, `Process`, or `Structural`). Topic is non-null exactly for answering models whose function is `Rules` or `Enforcement`. No ERROR strings or inferred labels are inserted. Per-model missing counts are in `manifest.json`.
+
+Section rows without a model answer: Gemini **60**; OpenAI **1**; Jev **0**.
 
-If you use this dataset, please cite:
+Consensus counts only models with valid answers. An answering model's inapplicable topic counts as a vote for not applicable; a missing model contributes no vote. A majority requires at least two matching votes. `topic_majority` null with `topic_votes` at least 2 means a majority for not applicable; at most 1 means no majority. All three agree when both vote counts equal 3. Confidence is reported separately for each model.
+
+## Human-audit agreement
+
+The released audit contains **250 excerpts**. Each excerpt has three human annotations.
+
+The following table reports excerpts with human majority consensus for each field. These are consensus eligibility rates, not pairwise or chance-corrected inter-annotator agreement statistics.
+
+| Human field | Eligible consensus / audit excerpts |
+|---|---:|
+| function | 226/250 (90.4%) |
+| topic | 216/250 (86.4%) |
+| confidence | 244/250 (97.6%) |
+
+Human function, topic, and confidence have separate eligibility flags and must be evaluated independently. An eligible blank human topic is a valid null; an ineligible blank is unscored. Joint evaluations require all relevant eligibility flags. Shared `audit_id` values should remain together when splitting related human-label data.
+
+The next table reports exact model agreement with eligible human consensus. A row is scored only when that field is human-eligible and the model returned a valid function. Null topics from answering models are valid not-applicable answers and are compared as such. Missing model answers reduce scored coverage, and do not enter the agreement denominator.
+
+| Model | Field | Exact matches / scored | Scored / eligible |
+|---|---|---:|---:|
+| Gemini | function | 193/226 (85.4%) | 226/226 (100.0%) |
+| Gemini | topic | 185/216 (85.6%) | 216/216 (100.0%) |
+| Gemini | confidence | 141/244 (57.8%) | 244/244 (100.0%) |
+| OpenAI | function | 177/226 (78.3%) | 226/226 (100.0%) |
+| OpenAI | topic | 172/216 (79.6%) | 216/216 (100.0%) |
+| OpenAI | confidence | 108/244 (44.3%) | 244/244 (100.0%) |
+| Jev | function | 183/226 (81.0%) | 226/226 (100.0%) |
+| Jev | topic | 164/216 (75.9%) | 216/216 (100.0%) |
+| Jev | confidence | 113/244 (46.3%) | 244/244 (100.0%) |
+
+## Collection and limitations
+
+The providers use the shared codebook and identical header-plus-content input, capped at 16,000 characters. Truncation counts are measured during the build and recorded in `manifest.json`.
+
+Only Jev supplies probability scores. They are preserved as float32 without normalization; provider rounding means sums may differ from 1. They are provider scores, not verified calibrated probabilities. Evaluate calibration on the human audit before interpreting them as calibrated probabilities.
+
+Confidence follows a rubric with Low precedence. Compare confidence within each source; the rubric can produce more Low responses than the human audit. Confidence agreement measures rubric-label matching, not calibration.
+
+The shared Tennessee Mayor-Aldermanic model charter has several place GEOIDs and therefore a null `jurisdiction_geoid`. Represented counties come exclusively from county harmonization. Washington, DC has `jurisdiction_type` `other`. Uncovered counties retain null document and jurisdiction fields.
+
+## Provenance
+
+`manifest.json` records input hashes, exact model and prompt metadata, measured counts, missing answers, source-code hashes, and deterministic checks against run ledgers. Unavailable actual run dates remain null with an explanation; annotation collection timestamps are not substituted for run dates.
+
+## License
+
+Declared dataset license: `cc-by-nc-4.0`.
+
+## Citation
 
 ```bibtex
 @article{peskoff2026freeing,
@@ -181,4 +195,4 @@ If you use this dataset, please cite:
   journal={arXiv preprint arXiv:2606.19334},
   year={2026}
 }
-```
+```

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