A Mismatch Between Open Science Practices and Intentions in North America: Barriers and Incentives for Early Career and Senior Researchers
Bibliographic record
Abstract
This study examines the attitudes and frequency of engagement with open science practices (OSPs) like data sharing among early-career (ECRs) and senior psychology researchers (non-ECRs) in North America. Given the growing emphasis on transparency and reproducibility in scientific research, understanding how these practices are perceived and implemented at different career stages is crucial for improving research quality. We analyzed survey data from 290 psychology researchers in Canada and the United States, looking at their attitudes toward OSPs and how often they engaged in such practices. Results show a gap between positive attitudes toward OSPs and their actual implementation. Content analysis of open-ended responses identified several barriers and incentives to OSPs. Despite widespread agreement on the value of transparency and collaboration, barriers such as career pressures, lack of training, fear of scrutiny, peer resistance, and long-established norms prevent many researchers—but especially ECRs—from fully embracing these practices. This gap between perception and practice highlights the need for changes in academic incentives and infrastructure to better support transparency, reproducibility, and collaboration. We conclude with recommendations to increase engagement in best practices and improve the credibility and transparency of psychological research. Data, study materials, and supplementary resources can be found at: https://osf.io/5mn2x/.
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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 | 0.041 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".