README.md

metadata

license: cc-by-nc-4.0
pretty_name: LOCUS-Fines
language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - text-classification
source_datasets:
  - LocalLaws/LOCUS-v1
tags:
  - legal
  - law
  - us-local-law
  - ordinances
  - fines
  - penalties
  - locus
configs:
  - config_name: default
    data_files:
      - split: train
        path: locus_fines_supplement.parquet

LOCUS-Fines

A fine-grained fines layer for LOCUS, the Local Ordinance Corpus for the United States. We annotate roughly 600,000 rows with detailed information about fines.

We use gemini-3.5-flash-lite for the annotations. Every dollar amount is checked against the original text to reduce hallucinations. Rarely, a number referring to something else, such as a number of months, may be miscategorized as a fine.

annotation_source

value	rows	meaning
LLM	632,005	annotated by the model
unchanged_LOCUS	1,579,511	not sent to the model; derived from LOCUS fields

Consumers who want only model-read rows can filter annotation_source == "LLM"; those who want a corpus-complete view use all rows.

Schema

column	type	description
state, source_jurisdiction_type, city, county	string	jurisdiction identity, verbatim from LOCUS-v1
function, header	string	section identity within the code, verbatim from LOCUS-v1
content_sha1	string	16-hex fingerprint of the section text (join key; see below)
fine_relevant	bool	the section states, references, or implies a monetary penalty
penalty_scope	enum/null	code_general (code-wide catch-all), chapter_general (chapter/article catch-all), specific (particular conduct); null when the section states no penalty of its own
penalty_stated	enum/null	amounts_here (penalty stated in this text), cross_reference (points elsewhere, e.g. "Penalty, see § 1-8"), implicit (conduct prohibited, no penalty mentioned)
fine_structure	enum/null	fixed (one flat amount) or fluid (maximum, range, discretion, or escalation)
fixed_amount	float/null	the flat amount when fine_structure = "fixed"
min_amount, max_amount	float/null	smallest / largest amount stated as a penalty ("not less than" / "not to exceed")
first_violation_amount, second_violation_amount, subsequent_violation_amount	float/null	amounts tied to offense counts
effective_min, effective_max	float/null	min / max across all six amount fields; the section's true penalty span (rolls tiered amounts into the range)
per_day_violation	bool	text explicitly makes each day a separate violation
jail_mentioned	bool	imprisonment stated as a penalty or alternative
penalty_nature	enum/null	criminal, civil, or both
extraction_flag	string/null	edge cases (fragment_incomplete, table_fragment, not_ordinance_text); null = an ordinary, complete section
grounded	bool/null	false when at least one amount field could not be found in the section's text and was removed; null on rule-derived rows
annotation_source	enum	provenance of the row: LLM or unchanged_LOCUS (see the provenance table above)

Invariant: penalty_scope is always null when penalty_stated = "implicit"; a section that states no penalty has no penalty scope.

Joining back to LOCUS-v1

The text is not duplicated here. Re-attach it by recomputing the same fingerprint on LOCUS-v1 and joining on the seven identity columns:

import hashlib
import pandas as pd
from datasets import load_dataset

locus = load_dataset("LocalLaws/LOCUS-v1", split="train").to_pandas()
fines = load_dataset("LocalLaws/LOCUS-Fines", split="train").to_pandas()

locus["content_sha1"] = [hashlib.sha1(c.encode()).hexdigest()[:16]
                         for c in locus["content"]]
keys = ["state", "source_jurisdiction_type", "city", "county",
        "function", "header", "content_sha1"]
merged = locus.merge(fines, on=keys, how="left")   # 1:1, no fan-out

The content_sha1 term makes the join exact even where different sections share a heading.

Limitations

• Model annotations reflect a single, cost-efficient model at temperature 0. Dollar amounts are verified against the source; the non-amount fields (penalty_scope, penalty_stated, fine_structure, penalty_nature, fine_relevant) are not independently verified and have not been human-reviewed at scale. Treat grounded = false and non-null extraction_flag rows with extra caution.
• Grounding covers amount fields only. It catches unverifiable or wrong-unit dollar figures, but does not check the categorical judgments.
• Sections chunked across multiple LOCUS rows are annotated per row; a penalty schedule split across chunks may be partially captured in each.
• Rule-derived rows (annotation_source = "unchanged_LOCUS") carry no amounts by construction.
• Boilerplate ordinance language repeats across jurisdictions, so counts of "N sections do X" overstate independent policy choices.

Citation

Built on and intended as a companion to LOCUS-v1. Please cite the LOCUS corpus (see LocalLaws/LOCUS-v1) alongside this supplement.