How Can the Methodology of Feminist Judgment Writing Improve Gender-Sensitivity in International Criminal Law?
Bibliographic record
Abstract
The Feminist Judgments Project has been utilised in a number of jurisdictions, including the UK, US, Canada and Australia, to critique real-life judicial judgments and to re-write these problematic judgments using feminist judging methodologies. This paper seeks to demonstrate the utility of the application of feminist judging methodologies to judgments and decisions from international criminal law mechanisms, with a specific focus on sexual and gender-based crimes, as a means to improve gender-sensitivity in international criminal judicial decision-making. Through an analysis of feminist judgments and feminist dissenting opinions from the UK, US and International Criminal Court, the main hallmarks of feminist judging are identified. The author uses the hallmarks of feminist judging to create her own Feminist Judgment based on a decision from the Prosecutor v Ongwencase before the International Criminal Court, to display the indeterminacy of judicial decision-making in international criminal law and to demonstrate how greater gender-sensitivity can be achieved at the International Criminal Court through feminist judicial reasoning.
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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 | 0.142 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".