Supporting robust, rigorous, and reliable reviewing as the cornerstone of our profession: Introducing a competency framework for peer review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it