| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 1M<n<10M |
| task_categories: |
| - text-classification |
| pretty_name: LOCUS v1.0 |
| tags: |
| - law |
| - legal-nlp |
| - local-government |
| - municipal-law |
| - ordinances |
| - classification |
| configs: |
| - config_name: default |
| 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 |
| --- |
| |
| # LOCUS v1.0 |
|
|
| 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). |
|
|
| ## 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. |
|
|
| ## Dataset Structure |
| The unit of analysis is a text chunk derived from local law documents. |
|
|
| ## Scale |
| Approximate scope for v1: `2,211,516` ordinances |
|
|
| ## Label Schema |
|
|
| ### Function |
| Allowed values: |
| - `Context` |
| - `Rules` |
| - `Process` |
| - `Enforcement` |
|
|
| ### 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` |
|
|
| ### Topic |
| Used only when `is_substantive = 1`. |
|
|
| Allowed values: |
| - `Buildings` |
| - `Business` |
| - `Nuisance` |
| - `Zoning` |
| - `Other` |
|
|
| ## 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 |
|
|
| ## 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 |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{peskoff2026freeing, |
| title={Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States}, |
| author={Peskoff, Denis and Barrow, Joe and Vu, Christopher and Davenport, Diag}, |
| journal={arXiv preprint arXiv:2606.19334}, |
| year={2026} |
| } |
| ``` |
