Thinking Differently about ‘False Allegations’ in Cases of Rape: The Search for Truth
Bibliographic record
Abstract
The myth ‘cry wolf’ continues to pose particular problems for campaigners, policy makers and practitioners. This paper subjects this myth, and the way in which it has been debated, to critical scrutiny with a view to suggesting an alternative and better way of challenging the presumption both in theory and in practice that women ‘cry wolf’. In reflecting on lessons learned that presume believability in establishing rapport from the treatment of children in sexual offence cases the paper suggests that such practices can maximise efficacy in the treatment of women in cases of rape. It concludes that by leaving accusatory language behind, complainants, practitioners and judicial parties may experience more successful pathways to truth.
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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 | 0.094 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.014 | 0.109 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.023 | 0.030 |
| 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".