Le Processus de Consultation et D'Evaluation Entourant les Nominations a la Cour Provinciale du Nouveau-Brunswick: Evolution Vers un Appareil Juridique Depolitise Favorisant le Developpement de la Communaute Acadienne de Cette Province
Bibliographic record
Abstract
The authors trace the effect of Acadian language rights on the process for the selection of provincial court judges in New Brunswick. The authors contend that Acadians were historically limited in their participation in the Canadian democratic process, and that now their right to political and legal participation must be ensured. Briefly, the history of Acadian political participation is traced, as is the emergence of minority language rights, both federally and in New Brunswick. In order for Acadians to be represented among the judiciary, the authors explain, equal educational opportunities were necessary for the proper formation of Acadian judicial candidates. The Supreme Court of Canada's recognition of the importance of preserving minority language rights is also noted. The authors then go on to explain the procedures, in place since 1988, for the selection of judges of the Provincial Court of New Brunswick. It is contended that this process has in consequence become less politicized. Finally, it is noted that this structure does not impede Acadians' participation in the judiciary.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".