More Than Mere Consent: A Novel Theory of Sexual Permission
Bibliographic record
Abstract
Sexual consent is something that is receiving more widespread attention in the face of the #MeToo movement. However, sexual consent as the gold standard of sex is misguided and emphasized to the extent that all other relevant areas of consideration in terms of sex are all but eliminated. In this thesis, I begin by focusing on the flaws of sexual consent. I argue sexual consent is flawed in theory as it conforms to the masculinist tradition of philosophy, it oversimplifies sex, and it attempts to be objective in the face of sex. Further, there is a misguided attempt to fit sex into one-size-fits-all normative ethical approaches. I then argue that any sexual education surrounding consent is ineffective and overruled by the pervasive and poor representations of sex in mainstream media and mainstream pornography. I will then resolve these flaws by arguing for more emphasis placed upon non-mainstream theories of sex, namely theories put forward by Ann Cahill and Quill Kukla. I will then argue for the consent-forward approach to be replaced by a care-forward approach, following care ethics and Joan Tronto’s four qualities of care. Finally, I will argue for an improvement of sexual education and sexual normalization, by fixing sexual education and improving representations of sex in mainstream pornography and mainstream media. In doing so, I will formulate an approach to sex that is more conducive to good sex than the consent-forward model.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".