Implementation and impact of the global access principles at the University of British Columbia: current successes and future challenges
Bibliographic record
Abstract
In 2007, the University of British Columbia (UBC) was the first university in Canada to establish and adopt global access (GA) principles. Toward implementing these principles, UBC then identified a set of strategies for providing affordable access to new UBC-developed technologies throughout low- and middle-income countries and among vulnerable populations. In this perspective, we provide an update of UBC's progress over the past 15 years made on several technologies that fall under the GA principles. The technologies reported on are wide-ranging, including an oral medication for the treatment of leishmaniasis; peptides for potential use against malaria, and various bacterial, viral and fungal infections; a portable vaccine cooler; a diagnostic technology to detect severe sepsis; and an SMS Messaging System to monitor and support patients with HIV, TB and COVID-19. We identify challenges faced by the researchers in implementing the GA principles for these technologies and potential solutions for overcoming them through creative licensing and partnerships with public and private sectors, governments, local companies, and communities. As the UBC example illustrates, universities across the globe have an opportunity to make a significant social impact on improving global health of vulnerable populations and on supporting local infrastructures for sustaining these improvements.
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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 | 0.050 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".