Bibliographic record
Abstract
Abstract It is a commonplace that sometimes our behavior imposes on others and that this is worth avoiding. However, just how we impose and why it might matter have not been much studied. Philosophers have started to rectify this. Matti Häyry and Amanda Sukenick address imposition as part of a defense of antinatalism (2023). More extensively, Fiona Woollard provides an account of imposition and contends that it is the foundation for the Doctrine of Doing and Allowing, according to which doing harm is more difficult to justify than allowing it (2015). The present project offers a theory of imposition, composed of an account of its nature and an explanation of why it is significant. I argue that the extant work on imposition has operated with inadequate understandings of its nature. While the antinatalist argument of Häyry and Sukenick can be preserved when combined with an adequate view of imposition, Woollard’s case for dda must be abandoned.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".