Bibliographic record
Abstract
The sacrifices of nurses in hard-hit cities during the early stages of the COVID-19 pandemic and of family caregivers for people with late-stage Alzheimer’s disease present two puzzles. First, traditional accounts of supererogation cannot allow for the possibility of making enormous sacrifices that make one’s actions supererogatory simply to do what morality requires. These caregivers, however, are doing their moral duty, yet their actions also seem to be paradigmatic cases of supererogation. I argue that Dale Dorsey’s new account of supererogation can solve this puzzle. Second, these caregivers often deny that they are heroic, but standard explanations of these assertions either diminish their sacrifice, say they are confused, or attribute to them a vice. If we want to understand them without diminishing them, we should instead see their denials as a response to what Beth DeVolder calls compulsory heroism. Compulsory heroism occurs when someone is foisted into the role of hero for doing their moral duty as a distraction from the social realities that make doing their duty involve inordinate sacrifice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".