MétaCan
Menu
← Back to cohort
Record W4405107060 · doi:10.55016/ojs/sppp.v17i1.79995

Technology Transfer: The Rise of the Entrepreneurial University

2024· article· en· W4405107060 on OpenAlexaboutno aff
Mark R. Huson, Randall Mørck

Bibliographic record

VenueThe School of Public Policy Publications · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationNegotiationBusinessWork (physics)MarketingConfidentialityTechnology transferValue (mathematics)Best practicePublic relationsEconomicsManagementPolitical scienceEngineeringInternational trade

Abstract

fetched live from OpenAlex

Canadian universities need better ways to commercialize their research and turn it into viable technologies. Typically, a researcher reports a potential breakthrough to her university through a confidential report. Then, technology arising from her findings can be licensed to an existing company or spun out and developed by a new spinoff. Much of this activity happens through the university’s technology transfer office (TTO). TTOs handle everything from IP evaluation and marketing to negotiating and managing licences. On average, Canadian university TTOs handle 5.11 invention disclosures per year, execute 2.93 licences and manage 3.83 university spinoffs. However, figures vary wildly between institutions, ranging from zero to double digits. Income from TTOs is just as variable, with some universities earning millions while others report net losses. Industry partners are often geographically distant from the universities they work with. Fewer than a third of one Canadian university’s licences are with companies in this country. This is because each technology has a best receptor in the private sector, and each business has a field to which it is best suited. To maximize the value of commercialization, technology should be developed by the entity with the highest values use case. However, there is no guarantee that universities and businesses will find the best assortative matches. Policy-makers may further disrupt things by insisting on local economic development. There is always a trade-off between getting IP to the best overall receptor, or the best local receptor. Information costs are one of the biggest barriers. The wide variety of research at any university makes it unlikely that a small TTO can support commercialization properly. Consolidating similar technologies in specific TTOs would allow for the concentration of subject matter expertise. To simplify things further, TTOs could let businesses come to them in pursuit of specific technologies. This could be done via web portals that push specific technologies or solutions for problems. This boutique approach will likely create better matches but does not guarantee a local match. It will also require a rethink of TTO staff compensation, with a focus on the qualityinstead of the quantity of matches. If governments are determined to use university research to grow the economy, they need to prepare the ground so there are local receptors. Areas with a lot of receptors need access to capital in the form of startup funding. If a technology requires continual input from its creators, capital and receptors are more likely to move to where the researchers are based. The solution is to increase technological inertia and the agglomerative nature of local tech ecosystems. The key is creating university spinoffs and keeping them local. The way forward involves creating a provincial office of scientific research and experimental development, using budgetary carrots and sticks to convince universities to change how their TTOs work and developing university programming to enhance the commercialization skills of subject matter experts. Creating a provincial-level refundable commercialization expense program, providing a well-curated portal listing funding and commercialization support, mandating registration/licensing of all fee-taking entrepreneurial support services and educating potential local angel investors all factor into the equation for success as well.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0280.015
Scholarly communication0.0200.008
Open science0.0020.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0180.002

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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe School of Public Policy Publications→Same topicEntrepreneurship Studies and Influences→French-language works237,207→