Bibliographic record
Abstract
Luck egalitarianism has been criticized for (1) condoning some cases of oppression and (2) condemning others for the wrong reason—namely, that the victims were not responsible for their oppression. Oppression is unjust, however, the criticism says, regardless of whether victims are responsible for it, simply because it is contrary to the equal moral standing of persons. I argue that four luck egalitarian responses to this critique are inadequate. Two address only the first part of the objection and do so in a way that risks making luck egalitarianism inconsistent. A third severely dilutes the luck egalitarian doctrine. A fourth manages to denounce some instances of oppression for the right reason, but at the same time permits other instances of oppression and condemns yet others for the wrong reason.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.110 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".