Towards inclusive funding practices for early career researchers
Bibliographic record
Abstract
Securing research funding is a challenge faced by most scientists in academic institutions worldwide. Funding success rates for all career stages are low, but the burden falls most heavily on early career researchers (ECRs) - young investigators in training and new principal investigators - who have a shorter track record and are dependent on funding to establish their academic career. The low number of career development awards and the lack of sustained research funding results in the loss of ECR talent in academia. Several steps in the current funding process, from grant conditions to the review process, play significant roles in the distribution of funds. Furthermore, there is an imbalance among certain research disciplines and labs of influential researchers that receive more funding. As a group of ECRs with global representation, we examined funding practices, barriers, facilitators, and alternatives to the current funding systems to diversify risk or award grants on a partly random basis. Based on our discussions, research, and collective opinions, we detail recommendations for funding agencies and grant reviewers to improve ECR funding prospects worldwide and promote a fairer and more inclusive funding landscape for ECRs.
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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 | 0.464 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.060 | 0.035 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".