Generating Intelligible Plumitifs Descriptions: Use Case Application\n with Ethical Considerations
Bibliographic record
Abstract
Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are\nused as summaries presenting all the steps of a judicial case. Information\nconcerning parties' identity, jurisdiction in charge of administering the case,\nand some information relating to the nature and the course of the preceding are\navailable through plumitifs. They are publicly accessible but barely\nunderstandable; they are written using abbreviations and referring to\nprovisions from the Criminal Code of Canada, which makes them hard to reason\nabout. In this paper, we propose a simple yet efficient multi-source language\ngeneration architecture that leverages both the plumitif and the Criminal\nCode's content to generate intelligible plumitifs descriptions. It goes without\nsaying that ethical considerations rise with these sensitive documents made\nreadable and available at scale, legitimate concerns that we address in this\npaper.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".