Data and Code for: Research Transparency Is on the Rise in Economics
Bibliographic record
Abstract
Has there been meaningful movement toward open science practices within economics in recent years? Discussions about changes in practices such as posting data and pre-registering analyses have been marked by controversy---including controversy over the extent to which change has taken place. This paper, based on the State of Social Science (3S) Survey, provides the first comprehensive assessment of awareness of, attitudes towards, perceived norms regarding, and adoption of open science practices within a broadly representative sample of scholars from economics . We observe a steep increase in adoption: as of 2017, over 90\% of scholars had used at least one such practice, rising from one quarter a decade earlier. This, however, masks substantial heterogeneity in the extent and pace of adoption by field and methodology. We also find that economists are generally aware of open science practices, and they are favorably inclined toward them, while underestimating the extent of support among their peers. Patterns are consistent with most scholars underestimating the trend toward open science in their discipline
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.017 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".