Bibliographic record
Abstract
One of the foremost problems of fiduciary law theory is the imprecise understanding of what a situation of conflict of interest involves. The mainstream contemporary legal literature on fiduciary duties is premised on the dual assumption that, on the one hand, humans are inclined to act self-interestedly and, on the other hand, they are too weak to consciously resist this urge while managing another person’s interests. Although these assumptions may be true in many cases of breach of fiduciary duties, they do not suffice to explain why fiduciary duties are imposed in situations where a fiduciary’s good faith and honesty cannot be questioned. This article proposes a novel understanding of the notion of conflict of interest. Building on insights from cognitive psychology, behavioural economics, and philosophy, this article defines a conflict of interest as the situation where a person, who has a duty to exercise judgment for the benefit of another, has an interest that tends to interfere with the proper exercise of his or her discretion. The emerging interdisciplinary theory of conflicts of interest shows that personal or extraneous interests interfere with a decision maker’s judgment in unpredictable ways, despite the decision maker’s honest efforts to keep them aside. This theory offers a more persuasive rationale for the strictness of fiduciary liability. It also offers a potent argument against the recent calls to relax the strict fiduciary regime in commercial contexts.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.056 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".