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CRIAQ and CARIC: An Innovation Journey - Insights on How to Build Successful Research and Development Collaborations in Aerospace: The Case of the Quebec and Canadian Ecosystems

2016· article· en· W2516382466 on OpenAlexaffabout
Cedric Prince, Clothilde Petitjean, Sofiane Benyouci, Rose Beaulieu, David Nolet

Bibliographic record

VenueJournal of Innovation Management · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsConsortium For Research and Innovation In Aerospace In Quebec
Fundersnot available
KeywordsAerospaceBusinessEngineeringEngineering managementPolitical scienceAerospace engineering

Abstract

fetched live from OpenAlex

The Consortium for Research and Innovation in Aerospace in Quebec (CRIAQ) and the Consortium for Aerospace Research and Innovation in Canada (CARIC) are organizations whose missions are to facilitate collaboration of researchers from the aerospace industry, academia and research centres, and to launch initiatives whose primary purpose is to promote responsive, impactful R&D. This letter presents the distinctive characteristics of these models and their impact on Quebec and Canada’s aerospace innovation culture.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0410.023
Scholarly communication0.0250.007
Open science0.0030.007
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.053
GPT teacher head0.314
Teacher spread0.261 · 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 designQualitative
DomainIncentives
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

Citations1
Published2016
Admission routes2
Has abstractyes

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