Bibliographic record
Abstract
Conflicts of interest arise when those who owe conscientious duties to others appear to have personal interests that might tempt them to subordinate those duties to their self-interest. Conflicts of interest are distinguishable from conflicts of commitment, which arise when individuals bear two or more mutually exclusive duties to others. If persons in conflicts of interest favor their self-interest, they may violate binding legal duties such as fiduciary duties. Conflicts arise when, for instance, medical practitioners refer their patients to other practitioners in exchange for payments (fee splitting), and when they gain secondary incomes such as from testing patients' samples in laboratories in which they hold ownership or investment interests. Financial dealings with commercial sponsors may place researchers in apparent conflicts of interest with research subjects. When conflicts of interest are unavoidable, they may be resolved by appropriate disclosure.
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 | 0.040 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.039 | 0.017 |
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".