“No signs of rape”: corroboration, resistance and the science of disbelief in the medico-legal jurisprudence of Bangladesh
Bibliographic record
Abstract
A major impediment to justice for rape in Bangladesh is the colonial rule of corroboration, which requires judges to verify the truthfulness of a rape complainant's testimony with other evidence. Medical evidence is the most commonly sought mode of corroboration and can be used to contradict the complainant's own testimony. The corresponding rule of resistance in turn guides how the rule of corroboration takes on a scientific character, whereby injuries in specific parts of the complainant's body are sought by doctors and judges as corroborative "signs of rape". If no "signs of rape" are found, this observation is then noted in the medical report and used to discredit the testimony of a rape complainant, by indicating that either the sexual intercourse was consensual or the rape accusation is false. This paper shows how the unfettered operation of these two rules gives birth to the "science of disbelief" in rape cases, whereby the institutional disbelief in a rape complainant's testimony is justified on ostensibly scientific grounds and largely restricts their right to seek justice. It illustrates how the science of disbelief was created and preserved through successive legal and institutional reforms in Bangladesh. This paper challenges the long-held yet seemingly unquestioned notion in Bangladesh that medical evidence should be the primary basis through which rape can be proved in court by analysing the pernicious jurisprudence and legal standards this assumption has created.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.097 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".