What motivates people to review articles? The case of the human‐computer interaction community
Bibliographic record
Abstract
Recruiting qualified reviewers, though challenging, is crucial for ensuring a fair and robust scholarly peer review process. We conducted a survey of 307 reviewers of submissions to the International Conference on Human Factors in Computing Systems (CHI 2011) to gain a better understanding of their motivations for reviewing. We found that encouraging high‐quality research, giving back to the research community, and finding out about new research were the top general motivations for reviewing. We further found that relevance of the submission to a reviewer's research and relevance to the reviewer's expertise were the strongest motivations for accepting a request to review, closely followed by a number of social factors. Gender and reviewing experience significantly affected some reviewing motivations, such as the desire for learning and preparing for higher reviewing roles. We discuss implications of our findings for the design of future peer review processes and systems to support them.
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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 | 0.207 | 0.543 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.031 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".