Ten Strategies to Foster Open Science in Psychology and Beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it