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Record W2021331626 · doi:10.1002/mde.1081

Cooperative R&D and the Canadian forest products industry

2003· article· en· W2021331626 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

VenueManagerial and Decision Economics · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpillover effectBusinessGovernment (linguistics)Value (mathematics)Industrial organizationEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract In the past decade the share of cooperative R&D has increased. In this paper, using a case study of the forest industry in Canada, the antecedents of cooperative R&D and the forms it take are investigated. We show how market failures are corrected in the industry largely through industry wide R&D consortia. The share of government funding to maintain the cooperation reflects the degree to which the consortia can appropriate the full value of their knowledge products (i.e. prevent spillover of innovations to non‐members in Canada and elsewhere). The case study indicates that the prime role of these nationwide consortia is the provision of potential access to R&D expertise, technological intelligence, and technology transfer services. The success and stability of these consortia depend on the degree to which their governance systems allow for better alignment of the costs and benefits that accrue to members from the consortia. Copyright © 2003 John Wiley & Sons, Ltd.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.027
GPT teacher head0.213
Teacher spread0.186 · 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