The Manitoba Court of Appeal, 2000-2004: Caseload, Output and Citations
Bibliographic record
Abstract
his article revives a series of annual pieces on the Manitoba Court of Appeal that appeared in Volumes 19 through 22 of this Journal, studying the Court through the collection and examination of statistics about various aspects of its activities.More correctly, there were three such series-one looking at "caseload and output", 1 a second looking at citation practices, 2 and a third looking at appeals from the Manitoba Court of Appeal to the Supreme Court of Canada.3 This article shall combine a brief consideration of all three of these elements.Because it is picking up on these themes after a silence of more than a decade, I will be considering not a single year but four successive terms ( 2000 through 2003 inclusive), the first four terms of the new century.4 The broader motivation is a profound curiosity about the work and role of the provincial courts of appeal, about what they do and how they do it.The work of the trial courts is conceptually straightforward: they represent a judicial first attempt to ascertain facts against the background of law and achieve a fair resolution of a specific dispute.The work of the Supreme Court of Canada, as a national high court of final appeal, is also conceptually straightforward: its *
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.041 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".