To blind or not to blind? What authors and reviewers prefer
Bibliographic record
Abstract
In order to inform discussions about possible changes to Medical Education's blinding policy, members of the journal's editorial board were interested in discovering reviewers' and authors' preferences with regard to the current double-blind policy and various alternatives. In September 2005, an 8-question, web-based survey was sent to all authors and reviewers who had submitted or reviewed a manuscript for Medical Education in 2003 and 2004 (n = 2632). The questions asked about authorship and reviewing experiences and preferences regarding 5 types of blinding procedure, from double-blinding to fully unblinded, open reviews. Following 2 electronic mailings, 838 surveys were completed. There was a range of experience among respondents, with a high proportion of experienced authors (49% with over 20 publications) and reviewers (41% with over 20 reviews). Overall, 68% of respondents preferred a review process that concealed author names and 72% preferred a process that allowed for concealment of reviewer names. Less experienced authors and reviewers were significantly more likely to prefer concealing author names, but even the most experienced respondents had a 54% preference for author concealment. Reasons for concealing identities included facilitating fairness and honesty in reviews and acknowledging the need to avoid personal conflicts or rivalries. Reasons for revealing identities included facilitating greater transparency and accountability, and a better understanding of the author's and reviewer's contexts and credentials. The Medical Education authors and reviewers who chose to respond to the survey voted strongly in favour of continuing the double-blinding procedure of concealing both author and reviewer identities during the review process.
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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 | 0.295 | 0.622 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".