‘For the most part it works’: Exploring how authors navigate peer review feedback
Bibliographic record
Abstract
BACKGROUND: Peer review aims to provide meaningful feedback to research authors so that they may improve their work, and yet it constitutes a particularly challenging context for the exchange of feedback. We explore how research authors navigate the process of interpreting and responding to peer review feedback, in order to elaborate how feedback functions when some of the conditions thought to be necessary for it to be effective are not met. METHODS: Using constructivist grounded theory methodology, we interviewed 17 recently published health professions education researchers about their experiences with the peer review process. Data collection and analysis were concurrent and iterative. We used constant comparison to identify themes and to develop a conceptual model of how feedback functions in this setting. RESULTS: Although participants expressed faith in peer review, they acknowledged that the process was emotionally trying and raised concerns about its consistency and credibility. These potential threats were mitigated by factors including time, team support, experience and the exercise of autonomy. Additionally, the perceived engagement of reviewers and the cultural norms and expectations surrounding the process strengthened authors' willingness and capacity to respond productively. Our analysis suggests a model of feedback within which its perceived usefulness turns on the balance of threats and countermeasures. CONCLUSIONS: Feedback is a balancing act. Although threats to the productive uptake of peer review feedback abound, these threats may be neutralised by a range of countermeasures. Among these, opportunities for autonomy and cultural normalisation of both the professional responsibility to engage with feedback and the challenge of doing so may be especially influential and may have implications beyond the peer review setting.
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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 | 0.005 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one teacher head, 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".