Ten Strategies to Foster Open Science in Psychology and Beyond
Bibliographic record
Abstract
The scientific community has long recognized the benefits of open science. Today, governments and research agencies worldwide are increasingly promoting and mandating open practices for scientific research. However, for open science to become the by-default model for scientific research, researchers must perceive open practices as accessible and achievable. A significant obstacle is the lack of resources providing a clear direction on how researchers can integrate open science practices in their day-to-day workflows. This article outlines and discusses ten concrete strategies that can help researchers use and disseminate open science. The first five strategies address basic ways of getting started in open science that researchers can put into practice today. The last five strategies are for researchers who are more advanced in open practices to advocate for open science. Our paper will help researchers navigate the transition to open science practices and support others in shifting toward openness, thus contributing to building a better science.
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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 | 0.110 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".