README.md

---
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}
}
```