Bibliographic record
Abstract
Some moral philosophers argue that our personal obligations to help address the collective economic, environmental and intergenerational crises of our time are imperfect duties. According to this view, we must all do our part to help resolve these crises, but each of us enjoys some latitude to decide how and when to do so. However, given the flexibility built into these duties, it is no surprise that many of us fail to take meaningful steps to act on them. In response to this problem, it has been argued that we should institutionalize our imperfect duties and so transform them into perfect duties – that is, legally enforceable duties that provide us each with specific guidance on what we must do. This paper explores whether this proposal is institutionally and normatively coherent as a matter of law. I argue that such perfected duties fit better within the institutions of public law rather than private law, but that some normative concerns remain for justifying such duties as legitimate public law duties.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".