Bibliographic record
Abstract
Conceptions of professionalism in medicine draw on social contract theory; its strengths and weaknesses play out in how we reason about professionalism. The social contract metaphor may be a heuristic device prompting reflection on social responsibility, and as such is appealing: it encourages reasoning about privilege and responsibility, the broader context and consequences of action, and diverse perspectives on medical practice. However, when this metaphor is elevated to the status of a theory, it has well-known limits: the assumed subject position of contractors engenders blind spots about privilege, not critical reflection; its tendency to dress up the status quo in the trappings of a theoretical agreement may limit social negotiation; its attempted reconciliation of social obligation and self-interest fosters the view that ethics and self-interest should coincide; it sets up false expectations by identifying appearance and reality in morality; and its construal of prima facie duties as conditional misdirects ethical attention in particular situations from current needs to supposed past agreements or reciprocities. Using philosophical ideas as heuristic devices in medical ethics is inevitable, but we should be conscious of their limitations. When they limit the ethical scope of debate, we should seek new metaphors.
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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.082 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".