The Innovation Journal: The Public Sector Innovation Journal, Vol. 10(2), article 22. What do lawyers think about judicial evaluation? Responses to the Nova Scotia Judicial Development Project 1
Bibliographic record
Abstract
As a contribution to evaluation practice, this paper reports on an evaluation of the Nova Scotia Judicial Development Project (NSJDP). Lawyers who completed evaluation questionnaires regarding the performance of judges responded to yet another questionnaire concerning the project. An analysis of their responses allows us to reach conclusions concerning the over-all evaluation process and specific issues of confidentiality, judicial independence and accountability. The NSJDP provided individual judges with feedback on their performance in areas of legal ability, impartiality, judicial management skills, disposition practices and comportment. It remains the only Canadian experience with the systematic evaluation of judicial performance in this format. Concerns for judicial independence shaped the development, marketing and implementation of the project. Nova Scotia lawyers affirmed the over-all evaluation initiative while noting some concerns for response burden and confidentiality. Most lawyers did not believe the project threatened judicial independence. The pilot project was considered a success in providing individual judges with useful information for further professional development, but has not moved past this pilot stage. The barriers to further implementation are identified as practical barriers of budgetary limitations, remaining reservations concerning judicial evaluation and the lack of innovation “champions ” within the Canadian provincial court systems. International awareness and interest in the NSJDP is noted.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".