Ethics of scientific peer review: Are we judging or helping the review recipients?
Bibliographic record
Abstract
Traditionally, ethics of a profession or organization are laid down by their pioneers, or subtly emerge over time as the organization advance. Getting conversant to these ethics requires teaching new or upcoming professionals, in order to avoid any form of misconduct, either deliberately or unknowingly. Peer review has been used as a quality control measure in the scientific community to ensure that only novel, high-quality and significant research work can be published. Typically, experienced and well respected scientists are selected to review the work of their peers or other upcoming scientists. Ideally, people who ethically qualify as reviewers, should have high reputation in terms of their ability to give objective and well-informed judgement, write constructive and helpful critique in a timely manner and, are honest and open in revealing any conflict of interest that may exist. The key objectives of peer review are two fold: 1) summative - to assess the quality of scholarly work, and 2) formative - to provide constructive feedback and thus, to mentor authors to become both better researchers, and better writers.
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 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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".