The Failure of Consent: Re-Conceptualizing Rape as Sexual Abuse of Power
Bibliographic record
Abstract
Introduction • 2 I. The Empirical Failure of Consent • 9 A. The Refusal to Criminalize Nonconsensual Sex as Rape • 10 B. The Affirmative Consent Standard’s Empirical Failure • 12 C. In re M.T.S.: The Swing of the Pendulum • 15 D. Affirmative Consent in Comparative Law: Canada • 23 II. From Empirical Failure to Normative Inadequacy • 29 A. Reasons for the Empirical Failure • 29 1. Failure to Align Social Norms with Legal Changes • 29 2. The Persistence of the “No Means No” Debate • 30 3. Prosecutorial Discretion and Social Norms • 32 4. Criminal Law’s Role in Changing Social Norms • 33 5. Interpreting Consent as Permission • 36 B. Reasons for the Normative Inadequacy • 37 1. Failure to Capture Harm and Injury • 38 2. Failure to Capture Criminal Wrongdoing • 40 3. Failure to Account for Complainants’ Narratives • 43 4. Failure to Account for Conflicting Considerations • 45 III. An Alternative Underpinning of Rape Laws: Sexual Abuse of Power • 47 A. Abandoning Criminal Regulation? • 47 B. The Misdirected Turn to Force • 48 C. Rejecting Both Consent and Force • 52 D. Re-Conceptualizing Rape as Sexual Abuse of Power • 53 1. What Is Rape? Revisiting the “Violence or Sex” Debate • 54 E. The Proposed Model’s Normative Strengths • 59
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.070 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".