Declaration of conflict of interest for reviewers in time of COVID-19 should be mandatory
Bibliographic record
Abstract
Dear Editor, We much appreciated the editorial by Sharma[1] showing the importance of the disclosure of conflict of interest (COI) in scientific research, particularly in the time of COVID-19. We agree that all the stakeholders of the publication process should be aware of the criticality of this major issue concerning publication ethics. This becomes more significant during this pandemic time, in which the research on COVID-19 could raise some ethical concerns. The body of COVID-19-related publications, which is massive and impressive, [2]the pressure and speed at which COVID-19 research is occurring, and the poor quality of the peer review process, which is often “questionable,” [3]may exacerbate the scientific fraud.[4] During this pandemic, the likelihood of honest error as well as of deliberate misconduct have been increasing. To date, the Retraction Watch website has published in its list 37 retractions, 3 temporarily retracted papers, and 3 expression of concerns.[5] In addition, many of the published papers are not peer-reviewed. A Reuters analysis of some of the most important servers (Google Scholar, bioRxiv, medRxiv, and ChemRxiv) indicated that 60% of studies are preprints, which are reporting nonpeer-reviewed information.[3] Certainly, much more attention should be payed by authors when they declare their disclosure of COIs on COVID-19-related publications, but we believe that COI should be mandatory for reviewers as well. Peer review process is the core of the scientific production process.[4] Some publishers, especially those supporting open peer review, during the peer review process, ask authors to declare their potential COI. Examples of competing interests include reimbursements, fees, funding, or salary received from an organization that may gain or lose financially from the publication of the manuscript, affairs concerning stocks or patents relating to the content of the manuscript or other financial or nonfinancial competing interests. In this time of COVID-19, other relevant competing interests could include any financial interests related to new drugs, treatment and vaccines in the fight against COVID-19. It is crucial, therefore, reviewers refrain from being politicized or polarized and strive toward scientific rigor in terms of correct methodology and veracity of findings. Reviewers should also be vigilant in identifying dishonest practices and flawed interpretations by unethical researchers.[6] Unfortunately, reviewer's COI declaration is not required by all the scholarly journals. In most of the cases, however, peer reviewers could hiding behind the “blind” peer review not to declare their potential COI, which is detrimental to publication ethics and effectiveness of the scientific work. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.041 | 0.446 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.076 | 0.072 |
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".