Bibliographic record
Abstract
The discovery that people far away are in bad shape seems to generate a sense of guilt on the part of many articulate people in our (wealthy) part of the world, even though they are no off now that we've heard about them than they had been before. I will take it as given that we are certainly responsible for evils we inflict on others, no matter where, and that we owe those people compensation. Not all similarly agree that it is not in general our duty to make other people better off, and therefore not in general our fault when people are not better off than they happen to be, even if perhaps we could have made them so by efforts of our own. Nevertheless, I have seen no plausible argument that we owe something, as a matter of general duty, to those to whom we have done nothing wrong. Still, morally commendable motives of humanity and sympathy support beneficence, and if we wish to call those there is something to be said for that, too. I shall, in fact, try to say it later in this essay. A further clarificatory point is in order: I also take it that if we did have any such duties, they would not be, as such, to people who are merely worse off than Americans don't owe anything to Canadians or Englishmen, even though Americans have a higher real income. Our subject, I presume, is people who are, by some reasonable criterion, badly off, and not merely off than we. It is not clear how we would identify this reasonable criterion, but I will assume that there are plausible answers; a duty to needy people, if we had one, would be to try to get them up to that relevant standard, rather than to a condition of equality with ourselves. I will say no more about egalitarianism here.1
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".