Recommendations for empowering early career researchers to improve research culture and practice
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
Early career researchers (ECRs) are important stakeholders leading efforts to catalyze systemic change in research culture and practice. Here, we summarize the outputs from a virtual unconventional conference (unconference), which brought together 54 invited experts from 20 countries with extensive experience in ECR initiatives designed to improve the culture and practice of science. Together, we drafted 2 sets of recommendations for (1) ECRs directly involved in initiatives or activities to change research culture and practice; and (2) stakeholders who wish to support ECRs in these efforts. Importantly, these points apply to ECRs working to promote change on a systemic level, not only those improving aspects of their own work. In both sets of recommendations, we underline the importance of incentivizing and providing time and resources for systems-level science improvement activities, including ECRs in organizational decision-making processes, and working to dismantle structural barriers to participation for marginalized groups. We further highlight obstacles that ECRs face when working to promote reform, as well as proposed solutions and examples of current best practices. The abstract and recommendations for stakeholders are available in Dutch, German, Greek (abstract only), Italian, Japanese, Polish, Portuguese, Spanish, and Serbian.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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