Bibliographic record
Abstract
The most prominent features of the landscape of fiduciary law are the no-conflict and no-profit rules. This paper aims to clarify the differences between existing accounts of these rules, and to propose my own argument for their justification. It first distinguishes between arguments that they have a deterrent function, and arguments that they have a prophylactic function. It goes on to argue that it is untenable, for two distinct reasons, to argue that these fiduciary norms have a deterrent function. By contrast, the paper argues that the no-conflict rules have a prophylactic function; they exist as a precaution against the exercise of duty-bound judgment in situations where improper influences may have unknown effects. It concludes by arguing that the no-profit rule has a different function: it is not a response to any kind of wrongdoing, but is a primary rule of attribution, that allocates profits and other benefits to the beneficiary at the moment they are acquired. This explains the otherwise puzzling features of the case law in this field. Moreover, these accounts of the no-conflict rules and the no-profit rule avoid all of the difficulties that beset deterrent accounts.
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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 | 0.000 |
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".