Bibliographic record
Abstract
In this reply to Campbell and Smith, I argue that my account is quite consistent with our moral intuitions about affirmative action policies. In particular, my theory does not claim that we all have an interest in being ‘completely free’ in a deliberative sense. So it does not commit us to the view that in cases of affirmative action, a lawmaker always has some reason to prevent discrimination against members of the more privileged group. Moreover, my account recognizes that whether someone has a right not to be discriminated against in certain circumstances depends on a complex array of factors that includes the interests of others, such as the beneficiaries of affirmative action programs. I then argue that the asymmetric treatment of certain grounds of discrimination in the law does not reflect any deep features of the injustice involved in discrimination. Instead, it reflects a judgment about the social contexts in which discrimination is likely to occur – a judgment that is entirely consistent with my theory.
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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.061 | 0.061 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".