Situational Awareness for Science Funders: Information Challenges and Solutions for Funding Agencies in the 21st Century
Bibliographic record
Abstract
About 25 years ago, one of our colleagues joined the Wellcome Trust, the world's second largest private biomedical funder. At the time, computers and the Internet were not a regular part of everyday work routines. Today, a quarter of a century later, the Wellcome Trust and other forward thinking funders are leading the way in integrating software, systems, and information technology into their funding processes. While not all research funders have been technologically proactive--some have only recently switched to electronic applications and others still operate with largely document-based processes-almost all funders experience some level of difficulty when it comes to translating technological advances into operational efficiencies and strategic insights. Also, although there are exceptions, funders generally do not share notes. That is scary. It is a rich and perhaps troubling irony that even while they invest billions of dollars in groundbreaking research to solve some of the world's greatest challenges, many funders struggle to find effective solutions to what can seem like pedestrian information challenges:
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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 | 0.072 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.040 | 0.044 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.024 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".