Bibliographic record
Abstract
Abstract Moral Animals offers a set of anthropological and conceptual foundations for moral theory before turning to the problem of overdemandingness or exigency as it afflicts contemporary egalitarianism. The first half of the book is devoted to a discussion of the bearing of evolutionary theory on ethics and metaethics. After arguing that morality presupposes and compensates for asymmetrical relations of advantage and social power, the author addresses the problem of objectivity, showing in what sense moral judgements are susceptible of confirmation, whether or not moral realism is tenable. In the second half of the book, a number of vexed issues in the theory of social justice, including the problems of affluence and the subordination of women, are examined. Taking the fair division of the co‐operative surplus as the basic problem of distributive justice, the author shows how most co‐operation between human beings fails to allocate goods to individuals and groups according to appropriate standards of need and merit. It is shown that neither the special nature of the first‐person standpoint, nor the importance of non‐moral projects and ambitions, nor the different needs, social understandings, competencies, and emotions of different persons and groups pose a serious challenge to the view that greater global equality in levels of well‐being, as well as greater equality between the sexes, is not only morally desirable but morally required.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".