La détermination de la peine dans les cas de filicide
Bibliographic record
Abstract
The purpose of our study is to understand the process of sentencing in cases of filicide from an analysis of criminal and legal criteria on which judges base themselves as well as from an analysis of penal functions aimed by the sentences issued for this type of homicide. The sample studied in this paper consists of fourteen sentencing judgments rendered by the judges at the sentencing. These judgments were all issued in Quebec between 1996 and 2008 inclusive. It is clear from our analysis that the guilty plea recorded by the accused, the lawyers' sentence suggestions, the method of filicide, repetition of misconduct or not in the parent, the number of victims, the age of the accused, the presence or absence of criminal history of the accused as well as the professional occupation of the latter may influence judges' decision concerning the sentence. These elements, however, do not necessarily carry the iv same weight in the process of sentencing from one judge to another. Nevertheless, we observe that the discretionary power the judges have does not seem to be used in a biased or inadequate manner. In fact, every sentence is clearly justified according to reasonable arguments and none is far from other sentences that have made jurisprudence.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".