Bibliographic record
Abstract
Reciprocity—put most generally—is the idea of actions-in-return that are not founded in voluntary agreements or contracts. Understood in this way, reciprocity can be one-on-one: the return of a kindness or the exchange of presents. But it need not be: pitching in to do one’s share of cooking for a potluck supper, cleaning up the local park, or contributing to the local public radio station. Here, the idea of reciprocity is doing one’s part to produce a common good, when—and especially because— others are doing theirs. The moral contribution of reciprocity in such cases is that pitching in rests not only on the idea of fair shares coupled with the recognition that the desired outcome will not be produced if too many fail to contribute, but also on the fact that others are doing their part. Free riders fail to do their fair shares, but this is not the full moral story. In addition, free riders let others down by failing to respond in return to the good efforts that others are making. Reciprocity in this sense has played a major role in contemporary bioethics discussions of pandemic planning. The most influential statement of the ethics of pandemic planning takes reciprocity to be a fundamental value, requiring that “society support those who face a disproportionate burden in protecting the public good.” Echoing the Canadian national anthem, the reciprocity of Stand on Guard for Thee includes not only compensation and protection for health care workers who take significant risks, as well as their families, but also responsiveness to people who are quarantined or otherwise limited in their activities in order to protect others. In addition, Stand on Guard
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".