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Record W4386294809 · doi:10.1186/s13731-023-00321-z

Research and technology organizations as entrepreneurship instruments: the case of the Institut National d’Optique in the Canadian optics and photonics industry

2023· article· en· W4386294809 on OpenAlexaffabout
Mahdi Khelfaoui, Luc Bernier

Bibliographic record

VenueJournal of Innovation and Entrepreneurship · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsUniversity of OttawaUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsEntrepreneurshipGovernment (linguistics)WorkforcePrivate sectorBusinessTelecommunicationsMarketingEngineeringEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

Abstract Research and technology organizations (RTOs) are studied in the innovation policy literature mainly as providers of R&D services and as intermediaries between universities and the private sector. Through the case of the Institut National d’Optique (INO), Canada’s leading RTO in the optics and photonics industry, we argue that RTOs can also act as entrepreneurs by generating technologies and commercializing them through licensing, technology transfers and spin-offs. By analyzing the broad range of activities undertaken by INO, we also discuss what characteristics make some RTOs more likely to embrace entrepreneurship than others. Those characteristics include the following: renewed access to government funding to build a strong in-house research infrastructure and scientific workforce; strategic R&D planning that incorporates commercial objectives and an environment that encourages a culture of entrepreneurship among employees; the ability to act as the driving force of a network of academic, government and private sector organizations. From a policy perspective, the INO case indicates that the main value of using RTOs as entrepreneurship instruments does not lie in profitability but rather in developing dynamic regional systems of innovation.

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 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)0.003
metaresearch head score (Gemma)0.003
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 score0.105
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.316
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2023
Admission routes2
Has abstractyes

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