GENOMICS, BIOTECHNOLOGY AND GLOBALHEALTH: THE WORK OF THE UNIVERSITY OF TORONTO JOINT CENTRE FOR BIOETHICS
Bibliographic record
Abstract
The new and rapidly advancing field of genomics and related biotechnologies has the ability to either improve or worsen global health inequities.In general, developing countries are left behind in the development of new technologies and advances in genomic medicine.In this view, the University of Toronto Joint Centre for Bioethics (JCB) through the Canadian Program on Genomics and Global Health has developed 25 research projects on capacity enhancement for developing countries for improving global health equity, including public health via genomics and related biotechnologies.One project with a great impact was the "Top Ten Biotechnologies for Improving Health in Developing Countries" for its influence in the "Grand Challenges in Global Health Initiative" foster by the Melinda Gates Foundation.Additionally, the UN Millennium Development Project has asked JCB to become the genomics working group for improving global health through genomics biotechnology and JCB has started by studying applications of genomics/biotechnologies in seven developing countries: Brazil, China, Cuba, Egypt, India, South Africa and South Korea, which may set examples for other developing nations.
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.
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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.016 | 0.032 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".