Bibliographic record
Abstract
It is now a platitude that sexual objectification is wrong. As is often pointed out, however, some objectification seems morally permissible and even quite appealing — as when lovers are so inflamed by passion that they temporarily fail to attend to the complexity and humanity of their partners. Some, such as Nussbaum, have argued that what renders objectification benign is the right sort of relationship between the participants; symmetry, mutuality, and intimacy render objectification less troubling. On this line of thought, pornography, prostitution, and some kinds of casual sex are inherently morally suspect. I argue against this view: what matters is simply respect for autonomy, and whether the objectification is consensual. Intimacy, I explain, can make objectification more morally worrisome rather than less, and symmetry and mutuality are not relevant. The proper political and social context, however, is crucial, since only in its presence can consent be genuine. I defend the consent account against the objection that there is something paradoxical in consenting to objectification, and I conclude that given the right background conditions, there is nothing wrong with anonymous, one-sided, or just-for-pleasure kinds of sexual objectification.
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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".