Open Science, Closed Peer Review?
Bibliographic record
Abstract
Open science initiatives have gained traction in recent years. However, open peer-review practices, i.e., reforms that (i) modify the identifiability of stakeholders and (ii) establish channels for the open communication of information between stakeholders, have seen very little adoption in economics. In this paper, we explore the feasibility and desirability of such reforms. We present insights derived from survey data documenting the attitudes of 802 experimental/behavioral economists, a conceptual framework, a literature review, and cross-disciplinary data on current journal practices. On (i), most respondents support preserving anonymity for referees, but views about anonymity for authors and associate editors are mixed. On (ii), most respondents are open to publishing anonymized referee reports, sharing reports between referees, and allowing authors to appeal editorial decisions. Active reviewers, editors, and respondents from the US/Canada are generally less open to transparency reforms.
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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 | 0.137 | 0.479 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".