Bibliographic record
Abstract
We are in the midst ofa profound revolution in health research, a revolution being driven by our emerging understanding of the molecular basis of life and human disease. This revolution is creating a century of health research, characterized by the convergence of virtually all disciplines, from mathematics to the social sciences and humanities. This convergence of disciplines is introducing radical changes and opportunities in the discovery and R&D process of health research. Academic health sciences centres (AHSCs) are strategically placed to contribute to, and benefit from, this revolution. To that end AHSCs require exceptional visionary leadership, a commitment to clinical and research excellence, a full appreciation of the complete tripartite mission of AHSCs, and the development of an outward-looking stance that includes the development of public policy. The federal governments clear and sustained commitment to health research, as demonstrated recently by the 15% increase to the Canadian Institutes of Health Research budget, provides an important opportunity for AHSCs to demonstrate their crucial role in both the development of an innovative and cost-effective healthcare system and to Canada's broader social and economic agenda.
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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".