Supporting robust, rigorous, and reliable reviewing as the cornerstone of our profession: Introducing a competency framework for peer review
Bibliographic record
Abstract
Abstract Peer review is a critical component toward facilitating a robust science in industrial and organizational (I-O) psychology. Peer review exists beyond academic publishing in organizations, university departments, grant agencies, classrooms, and many more work contexts. Reviewers are responsible for judging the quality of research conducted and submitted for evaluation. Furthermore, they are responsible for treating authors and their work with respect, in a supportive and developmental manner. Given its central role in our profession, it is curious that we do not have formalized review guidelines or standards and that most of us never receive formal training in peer reviewing. To support this endeavor, we are proposing a competency framework for peer review. The purpose of the competency framework is to provide a definition of excellent peer reviewing and guidelines to reviewers for which types of behaviors will lead to good peer reviews. By defining these competencies, we create clarity around expectations for peer review, standards for good peer reviews, and opportunities for training the behaviors required to deliver good peer reviews. We further discuss how the competency framework can be used to improve peer reviewing and suggest additional steps forward that involve suggestions for how stakeholders can get involved in fostering high-quality peer reviewing.
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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 | 0.266 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".