On a Promise or on the Game: What's Wrong with Selling Consent?
Bibliographic record
Abstract
Abstract Is selling sex a service like any other? Philosophers have given a range of answers to this question: (a) sex has a specific value that is debased by commercial markets in sex; (b) sex work is a service like any other; (c) markets in sex perpetuate structural systems of inequality. This article takes seriously the suggestion that there is something special about sex itself which raises a specific set of concerns when traded for money. The challenge is to explain this without drawing on contentious essentialist claims about the value of sex. It proceeds by analysing a parallel between sexual promises and selling sexual consent. On an expectational theory of promising, commercial agreements to sex generate obligations in a way that is normatively analogous to sexual promises. Understanding the normative release conditions for such assurance‐providing agreements provides a way of analysing the justifiability of various ways of enforcing such agreements. I argue that the release conditions for agreements involving sex are not conducive to being codified under typical forms of service contract. As such, regulation aimed at legitimising sex work must provide adequate protections to workers without codifying it under typical forms of service contract.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".