Reimagining the Judiciary
Bibliographic record
Abstract
Abstract This book examines the factors that facilitate women’s representation on high courts worldwide. Diverse courts improve collective decision-making, strengthen public confidence in the judiciary and judicial decisions, and broaden access to the judicial process. Taken together, domestic and international factors explain women’s representation. These influences include judicial pipelines, domestic institutions including selection processes, and international expectations about gender equity. These explanations are evaluated using an original dataset, which includes both men and women appointed to high courts in all regions of the world. Pathways and processes are examined in-depth through five case studies: Canada, Colombia, Ireland, South Africa, and the United States. Taking a multi-method approach, the book combines insights from a cross-national, time-serial dataset with case studies drawing on fieldwork. Women are being appointed to high courts in greater numbers across every region of the world, and political and legal institutions provide context for where the gains are earliest and strongest. The findings suggest a chain of favorable promoters for women’s representation on high courts: new norms of gender equality encourage the reimagining of the judiciary; advocacy organizations challenge the status quo; and windows of opportunity enable change.
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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 | 0.002 | 0.004 |
| 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 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".