“That Time of Month:” Premenstrual Dysphoric Disorder in the Criminal Law-Another Look
Bibliographic record
Abstract
This paper argues that women suffering from pre-menstrual dysphoric disorder (PMDD) ought to have available to them a range of legal defences that accurately reflect culpability. As PMDD focuses primarily on emotional mood and behavioural symptoms as opposed to physical manifestations of the premenstrual period, legal treatment of PMDD can be usefully compared to other “disordered states” that affect mental capacity, rationality and intent. Evolution of PMDD as a distinctive form of psychiatric disorder warrants a new consideration of the dual feminist concerns about the invisibility of women in criminal law theory and the undue labeling of all women. This article considers the application of criminal law defenses in light of newer research characterizing pre-menstrual mental disorder as a dynamic psychiatric and physiological state with shifting determinants that may be experienced differently over time. Ultimately, criminology must grapple with developing an account of women’s criminality that reflects accurately women’s lives lived within the sometimes overwhelming experience of biopsychosocial stressors. Reviewing PMDD in light of these concerns supports an enhanced understanding of the dynamics between women’s mental health and culpability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".