Debating Judicial Appointments in an Age of Diversity
Bibliographic record
Abstract
What should be the primary goals of a judicial appointments system, and how much weight should be placed on diversity in particular? Why is achieving a diverse judiciary across the UK taking so long? Is it time for positive action? What role should the current judiciary play in the appointment of our future judges? There is broad agreement within the UK and other common law countries that diversity raises important questions for a legal system and its officials, but much less agreement about the full implications of recognising diversity as an important goal of the judicial appointments regime. Opinions differ, for example, on the methods, forms, timing and motivations for judicial diversity. To mark the tenth anniversary of the creation of the Judicial Appointments Commission (JAC) in England and Wales, this collection includes contributions from current and retired judges, civil servants, practitioners, current and former commissioners on the JAC and leading academics from Australia, Canada, South Africa and across the UK. Together they provide timely and authoritative insights into past, current and future debates on the search for diversity in judicial appointments. Topics discussed include the role and responsibility of independent appointment bodies; assessments of the JAC's first ten years; appointments to the UK Supreme Court; the pace of change; definitions of 'merit' and 'diversity'; mandatory retirement ages; the use of ceiling quotas; and the appropriate role of judges and politicians in the appointments process.
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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 | 0.083 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.034 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 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".