Editorial: A critical review of peer review: The need to scrutinize the"gatekeepers" of research in exercise physiology
Bibliographic record
Abstract
I have developed as an educator and researcher accepting the premise that any system of peer review was unquestionably good. An explanation for this belief can be based, in part, on the mentor system within academia. After all, we can be molded as students to reflect the attitudes and professional interpretations of those we hold in high esteem. In addition, a summary of the historical development of peer review (see latter section) reveals that the process flourished relatively recently. Consequently, the more senior scientists of today who have and continue to function as mentors to many of our "younger" researchers, can recognize and remember the time of the transition in science towards an organized editorial peer review system for research manuscripts and grant submission ...
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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 | 0.141 | 0.426 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.032 | 0.009 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".