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
We propose that PhD and post-doctoral researchers are a strong, untapped resource with the potential to make a real contribution to global health research (GHR). However, we raise some ethical, institutional and funding issues which either discourage new researchers from entering the field or diminish their capacity to contribute. We offer a number of recommendations to Canadian academic and non-academic institutions and funders, and aim to generate discussion among them about how to overcome these constraints. We need changes in the way graduate research is organized and funded, to create opportunities to work collaboratively within established low- and middle-income country (LMIC)/Canadian research partnerships. We urge changes in the way institutions fund, recognize, value and support GHR, so established researchers are encouraged to develop long-term LMIC relationships and mentor new Canadian/LMIC researchers. We ask funders to reconsider additional GHR activities for support, including strategic training initiatives and dissemination of research results. We also encourage the development of alternative institutions that can provide training and mentoring
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it