---
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](https://huggingface.co/datasets/LocalLaws/LOCUS-v1), 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:

```python
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](https://huggingface.co/datasets/LocalLaws/LOCUS-v1))
alongside this supplement.