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Record W2018151718 · doi:10.1093/scipol/scu080

Looking under the street light: Limitations of mainstream technology transfer indicators

2015· article· en· W2018151718 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScience and Public Policy · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamFraming (construction)Technology transferPublic policyTechnology policyPublic administrationPolitical scienceRegional sciencePublic relationsBusinessEconomicsEconomic growthSociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.267
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it