Robson Crim: A Vision for Multivalent Interrogations of Criminal Law
Bibliographic record
Abstract
t is an exciting time for Canadian criminal law scholarship.There are any number of critical matters to examine, from issues of legislative reform, serious concerns relating to the scope of police powers in conducting investigations and their impact on constitutional rights and values, to the scope of criminal offences and defences.To explore these and many other significant topics, there is a variety of excellent venues to publish within Canada for scholars and practitioners in our communities, including practitioner journals, short submission reviews, criminology journals, and traditional law journals.When we met with the editorial team at the Manitoba Law Journal (MLJ) we did notice that there was a lacuna in Canada's scholarly criminal law realms.We wanted to develop a venue where scholars of criminal law, criminal justice, and criminology could openly discuss legal issues of significant import -a space where scholars could debate criminal law practice, theory, philosophy, and, also, provide an intellectual home that would welcome cognate disciplines to engage in these debates.In short, we wanted to establish a leading location to host national and international conversations on criminal law and justice, while at the same time allowing for the progression of the MLJ's goal to provide interesting insights to the local and national bar.This is neither the first, nor the last time that the MLJ has interrogated issues of criminal law.From jury work, plea bargaining, matters of
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.028 | 0.140 |
| Scholarly communication | 0.048 | 0.029 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 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".