Bibliographic record
Abstract
Abstract Women and the LGBT (lesbian, gay, bisexual, and transgender) community in Russia and Turkey face pervasive discrimination. Only a small percentage dare to challenge their mistreatment in court. Facing domestic police and judges who often refuse to recognize discrimination, a tiny minority of activists have exhausted their domestic appeals and then turned to their last hope: the European Court of Human Rights (ECtHR). The ECtHR, located in Strasbourg, France, is widely regarded as the most effective international human rights court in existence. Russian citizens whose rights have been violated at home have brought tens of thousands of cases to the ECtHR in the last 20 years. But only one of these cases resulted in a finding of gender discrimination—and that case was brought by a man. By comparison, the Court has found gender discrimination more frequently in decisions on Turkish cases. Courting Gender Justice explores the obstacles that confront those who try to use domestic and international law to fight gender and sexual orientation discrimination in Russia and Turkey, and sheds light on the factors that make legal victories possible both at home and abroad. Based on interviews with human rights and feminist activists and lawyers in both countries, this engaging book grounds the law in the experiences of individual people fighting to defend their rights.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".