How Institutions Influence the Appointment of Women to High Courts
Bibliographic record
Abstract
Abstract Chapter 5 sets out the formal and informal institutions that, collectively, comprise the selection process for the highest courts in five countries (Canada, Colombia, Ireland, South Africa, and the United States). Limiting the focus to formal rules of selection overlooks informal institutions (norms and practices) that constrain and enable the choices of selectors. Selection often rests on identifying a list of potential nominees based on informal networks, which have historically been composed of men. Across country cases, gendered networks and gendered ideas about qualifications often act as filters to hinder the appointment of women. When selectors or their key advisors decide to do so, they can disrupt reliance on these traditional networks by looking beyond the usual suspects as they draw up their shortlists. The chapter also illuminates the contexts in which electoral accountability and incentives matter. When selectors perceive electoral benefit from selecting a woman, and can be held accountable by their electorate, they are more likely to do so. In the context of pressure to select a woman, judicial nominating commissions and affirmative legal language can also increase women’s representation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".