Academic Primer Series: Key Papers About Peer Review
Bibliographic record
Abstract
INTRODUCTION: Peer review, a cornerstone of academia, promotes rigor and relevance in scientific publishing. As educators are encouraged to adopt a more scholarly approach to medical education, peer review is becoming increasingly important. Junior educators both receive the reviews of their peers, and are also asked to participate as reviewers themselves. As such, it is imperative for junior clinician educators to be well-versed in the art of peer reviewing their colleagues' work. In this article, our goal was to identify and summarize key papers that may be helpful for faculty members interested in learning more about the peer-review process and how to improve their reviewing skills. METHODS: The online discussions of the 2016-17 Academic Life in Emergency Medicine (ALiEM) Faculty Incubator program included a robust discussion about peer review, which highlighted a number of papers on that topic. We sought to augment this list with further suggestions by guest experts and by an open call on Twitter for other important papers. Via this process, we created a list of 24 total papers on the topic of peer review. After gathering these papers, our authorship group engaged in a consensus-building process incorporating Delphi methods to identify the papers that best described peer review, and also highlighted important tips for new reviewers. RESULTS: We found and reviewed 24 papers. In our results section, we present our authorship group's top five most highly rated papers on the topic of peer review. We also summarize these papers with respect to their relevance to junior faculty members and to faculty developers. CONCLUSION: We present five key papers on peer review that can be used for faculty development for novice writers and reviewers. These papers represent a mix of foundational and explanatory papers that may provide some basis from which junior faculty members might build upon as they both undergo the peer-review process and act as reviewers in turn.
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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 | 0.085 | 0.359 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.102 | 0.093 |
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".