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THE ALLOCATION OF RESOURCES TO COOPERATIVE AND NONCOOPERATIVE R&D*

2004· article· en· W3123109501 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.

Bibliographic record

VenueAustralian Economic Papers · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversity of Ottawa
Fundersnot available
KeywordsCournot competitionMicroeconomicsSubsidyDuopolyEconomicsInformation sharingComputer science

Abstract

fetched live from OpenAlex

The precompetitive R&D literature has viewed cooperative and noncooperative R&D as substitutes. In this paper a more realistic approach is taken, where both cooperative and noncooperative R&D are performed in parallel. In the first stage, firms determine the optimal investments in both types of R&D and in the second stage they compete in output. It is found that information sharing between cooperating firms contributes not only to cooperative R&D, but also to noncooperative R&D. The two types of R&D reinforce each other. The level of cooperative R&D may be higher or lower than noncooperative R&D. In a Cournot duopoly, the share of cooperative R&D lies between 20% and 80% of total R&D and this share increases with spillovers and information sharing. It is always optimal to subsidize half the costs of cooperative R&D, while the subsidy to noncooperative R&D is unchanged from the standard model. Consumers prefer intermediate levels of spillovers and information sharing, while firms prefer higher levels of spillovers, which entail lower levels of information sharing.

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)0.000
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 score0.970
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.263
Teacher spread0.225 · 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