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Record W2111901522 · doi:10.5172/impp.2006.8.3.296

The impact of industry associations: Evidence from Statistics Canada data

2006· article· en· W2111901522 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

VenueInnovation · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEnablingWork (physics)BusinessKnowledge managementEmpirical evidenceMarketingIndustrial organizationComputer scienceEngineeringPsychology

Abstract

fetched live from OpenAlex

SummaryIndustry associations are nonprofit organizations with specialized knowledge and capabilities that typically perform innovation enabler roles. I present Statistics Canada data that show that industry associations have a strong impact on the ability of Canadian firms to innovate. In the interests of informing further empirical work on the contributions of organizations that work to enable innovation, I develop theory to describe how organizations that perform innovation enabler roles are expected to contribute to the ability of firms to innovate. I also describe how the measurement guidelines of the Frascati and Oslo Manuals published by the Organization for Economic Co-operation and Development make it difficult to observe the existence and impact of nonprofit organizations that perform innovation enabler roles.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.153
GPT teacher head0.322
Teacher spread0.169 · 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