Looking under the street light: Limitations of mainstream technology transfer indicators
Bibliographic record
Abstract
This study investigates the use of university technology transfer reporting standards developed under the aegis of the US-based Association of University Technology Managers (AUTM) in Canada. Given the importance to policy-makers internationally of improving the contributions of universities in transferring technology to industry, these indicators are regarded as critical to informing the policy debate. We analyze federal science and technology policy and identify how these metrics have influenced the framing of policy problems and alternatives. Next, a micro-level analysis of Canada’s largest research university unveils several major weaknesses of the survey. Our study points to the need for a more critical use of the AUTM licensing data in the Canadian policy debate, and provides recommendations on the future development of these indicators and their use in public policy.
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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 | 0.123 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.034 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".