Bibliographic record
Abstract
hen we assumed editorship of the Manitoba Law Journal (MLJ) in 2010, our mission was to produce informed, diverse and timely discussions that would focus on events involving or highly relevant to our own community. 1 There are close to a million people living in Manitoba.The statutes and court cases of this province can and do affect their lives, sometimes in fundamental ways.That alone should be sufficient to justify having a venue for informed and independent commentary.A society needs critical commentary on how it is being governed, why, and what the future might or should look like.A law journal focused on our own province need not be provincial.It can bring to bear insights from many personal and philosophical perspectives; it can draw on learning from many disciplines, including history, philosophy, sociology, economics, and psychology.Developments in other jurisdictions can be a powerful source of understanding.Conversely, the study of legal events in Manitoba can contribute to many disciplines and their study in many places throughout the world.Our society is diverse and complex.Immigrants from all over the world have come here.It remains the home for many Indigenous communities.It has anglophone and francophone communities, and the legal system offers services in both official languages.A large part of the population lives in a modern urban centre, but Manitoba continues to have dynamic
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.236 | 0.142 |
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".