‘May it Please the Court’. Forming Sexualities as Judicial Virtues in Judicial Swearing-in Ceremonies
Bibliographic record
Abstract
Book synopsis: Does gender make a difference to the way the judiciary works and should work? Or is gender-blindness a built-in prerequisite of judicial objectivity? If gender does make a difference, how might this be defined? These are the key questions posed in this collection of essays, by some 30 authors from the following countries; Argentina, Cambodia, Canada, England, France, Germany, India, Israel, Italy, Ivory Coast, Japan, Kenya, the Netherlands, the Philippines, South Africa, Switzerland, Syria and the United States. The contributions draw on various theoretical approaches, including gender, feminist and sociological theories. The book's pressing topicality is underlined by the fact that well into the modern era male opposition to women's admission to, and progress within, the judicial profession has been largely based on the argument that their very gender programmes women to show empathy, partiality and gendered prejudice - in short essential qualities running directly counter to the need for judicial objectivity. It took until the last century for women to begin to break down such seemingly insurmountable barriers. And even now, there are a number of countries where even this first step is still waiting to happen. In all of them, there remains a more or less pronounced glass ceiling to women's judicial careers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".