Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
HFEPX · Eval paper review
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
Denis Peskoff, Joe Barrow, Christopher Vu, Diag Davenport
Published
Jun 17, 2026
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Jun 17, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
Abstract
Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)None explicit
Validate eval design from full paper text.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
Benchmarks / Datasets
provisional (inferred)Not extracted
No benchmark anchors detected.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
Reported Metrics
provisional (inferred)Not extracted
No metric anchors detected.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Progress in legal AI increasingly depends on access to authoritative legal text at scale."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: No benchmark names detected in abstract.
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: No explicit eval keywords detected.
- Potential metric signals: No metric keywords detected.
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Progress in legal AI increasingly depends on access to authoritative legal text at scale.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Progress in legal AI increasingly depends on access to authoritative legal text at scale.
- Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances.
- Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.