| --- |
| 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. |
| |
| |
