Scientific journals and conflict of interest disclosure: what progress has been made?
Bibliographic record
Abstract
The article addresses the failure of the scientific community to create an effective mechanism to protect the integrity of the scientific literature from improper influence by vested interests. The seriousness of this threat is increasingly recognized. Scientists willing to distort scientific research to serve vested interests receive millions of dollars for their services. Organizations such as the International Committee of Medical Journal Editors, the World Association of Medical Editors and the Committee on Publication Ethics (COPE) have launched initiatives to establish international standards for Conflict of Interest (COI) disclosure. COPE requires its 7,000 member journals to comply with its Code of Conduct for Journal Editors. While these initiatives are encouraging, they are internal educational endeavours only. Five examples are given showing failure of COPE member journals to comply with COPE's Code of Conduct. While COPE offers a complaint process, it involves only discussion and voluntary compliance. COPE neither polices nor enforces its Code. Instead of the current feeble, un-resourced process, which delivers neither transparency nor accountability, the article proposes the creation of a mechanism that will employ specific, effective measures to address contraventions of COI disclosure requirements.
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 | 0.069 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.029 | 0.038 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".