University Technology Transfer Has Failed to Improve Access to Global Health Products during the COVID-19 Pandemic
Bibliographic record
Abstract
Publicly funded research has contributed enormously to many products that were developed in the face of the COVID-19 pandemic. Yet universities' technology transfer practices have failed to ensure that these products are available in low- and middle-income settings. Drawing upon the example of the lipid nanoparticle delivery technology - which was developed in and around the University of British Columbia in Vancouver, BC, and incorporated into the Pfizer/BioNTech COVID-19 vaccine - we show the divide between the university's stated principles to serve global health and technology transfer in practice. We outline three policy actions to realign universities' technology transfer practices in the service of global health.
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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 | 0.044 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".