Bibliographic record
Abstract
Abstract This is a book about duties to help others. When does one have to sacrifice life and limb, time and money, to prevent harm to others? When must one save more people rather than fewer? These questions arise in emergencies involving nearby strangers who are drowning or trapped in burning buildings. But they also arise in everyday life, in which one has constant opportunities to give time or money to help distant strangers in need of food, shelter, or medical care. With the resources available, one can provide more help or less. This book argues that it is often wrong to provide less help rather than more, even when the personal sacrifice involved makes it permissible not to help at all. It shows that helping distant strangers by donating or volunteering is morally more like rescuing nearby strangers than most of us realize. The ubiquity of opportunities to help others threatens to make morality extremely demanding, and the book argues that it is only thanks to adequate permissions grounded in considerations of cost and autonomy that one may pursue one’s own plans and projects. It concludes that many are required to provide no less help over their lives than they would have done if they were effective altruists.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".