Limitations in a rapid environmental scan of global health research expertise point to the need for more open data
Bibliographic record
Abstract
We thank Gyorkos [1] for commenting on our rapid environmental scan of global health research expertise in Canada [2].Gyorkos argues that our scan was not comprehensive because we used too few data sources to measure Canadian global health research inputs, activities and outputs.Limitations in data, which were noted in our published study [2], highlight the current challenge of conducting rapid research within short policy windows using publicly available data sources in Canada and point towards opportunities for improving data infrastructure across countries.First, in line with our rapid approach, we made choices that resulted in what we believe to be the most rigorous environmental scan possible within the available resources and a set 2-month policy window.This meant that we were limited to drawing on publicly available administrative datasets and were prevented from collecting new data.For global health research inputs, we focused our analysis on data from Canada's largest funder of global health researchthe Canadian Institutes of Health Researchas other Canadian funding agencies do not make their global health research funding data readily available.Gyorkos additionally flags our omission of funding data from the Bill and Melinda Gates Foundation, the Global Fund to Fight AIDS, Tuberculosis and Malaria
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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 | 0.059 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".