MétaCan
Menu
Back to cohort
Record W2129955620 · doi:10.1093/reseval/rvv005

The impacts of research grants to community colleges. Evidence from the Technological Research Assistance Program in Quebec, Canada

2015· article· en· W2129955620 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

VenueResearch Evaluation · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsInstitut National de la Recherche ScientifiqueÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsCrowdsRevenueInvestment (military)Technology transferBusinessResearch programPublic fundingPolitical scienceEconomic growthFinanceEconomicsPublic administrationComputer science

Abstract

fetched live from OpenAlex

This article presents the findings of a study on the effects of the Technological Research Assistance Program (TRAP) on the primary activities of Quebec’s College Technology Transfer Centres (CTTCs). A database on the distribution of research grants and the activities of CTTCs has been created for this purpose. Then, panel data analysis techniques have been used to measure the effects of TRAP. The study results indicate that TRAP has a positive effect on the revenue of CTTC research projects. However, the increase in research revenue due to this program is slightly lower than the amount of the grant, which suggests that public funding for research partially crowds out private funding. Furthermore, the study suggests that the wise use of research assistance programs could stimulate research investment and, as a result, accelerate the advancement of knowledge and technological 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.

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)0.147
metaresearch head score (Gemma)0.081
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1470.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.663
GPT teacher head0.528
Teacher spread0.135 · 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