Entrepreneurial vision will define health/medical innovation at the University of Toronto over the next decade
Bibliographic record
Abstract
If I had been invited to write this article two decades ago, here at the University of Toronto, my definition of innovation for the University of Toronto would have been very similar to what the University aspired to for the previous 50 years, back to 1950. The vision at that time was to generate knowledge creation by training students via the process of curiosity-driven discovery. This prepared them for a singular career path in research by cultivating their direction towards academia within the post-doctoral pathway. In that mindset, discovery was only shared with the public through peer reviewed publications, housed within the ivory towers of universities, far out of reach from the entrepreneurs of the day. The role of the Canadian University was to impact academia. If society benefited from innovative discoveries, it would happen by random osmosis, as it was most certainly not a deliberate mandate of universities to prepare the entrepreneurial minds of scientific translation to in turn deliver the fruits of discoveries for society’s benefit. Rather, that task would be relegated to the established medical and pharmaceutical industry. However, dramatic shifts have occurred in our fields of health sciences and medical care over the last two decades. These have turned the ivory towers of medical science in Canada onto their side.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".