Bargaining for Expedience: The Overuse of Joint Recommendations on Sentence
Bibliographic record
Abstract
Abstract It is often stated that plea-bargaining is an indispensable part of a fair and efficient criminal justice system. By observing sentencing hearings in the Provincial Court of Manitoba this thesis shows that some form of plea bargaining is involved in a substantial majority of cases. Almost half of these plea bargained matters resulted in joint recommendations on sentence. However, the vast majority of these joint recommendations did not involve a true plea bargain. In this limited study, it was observed that the presiding judge accepted all joint recommendations as presented by counsel. One of the goals of plea bargaining is to arrive at joint recommendations on sentence. Though lawyers on both sides of the courtroom may perceive an advantage to joint recommendations, for the accused these advantages may be illusory. Judges routinely accept joint recommendations despite not being the progeny of true plea bargains involving a quid pro quo. This research suggests that the vast majority of joint recommendations are born of cultural expedience rather than as a result of true plea bargains. These cultural joint recommendations encroach significantly on the judicial function and may erode public confidence in the administration of justice. The continued proliferation of cultural joint recommendations may further entrench a culture of expedience in our criminal justice system and could potentially lead to higher sentences for offenders.
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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 | 0.033 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".