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International R&D Networks

2011· article· en· W3125263929 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

VenueReview of International Economics · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPairwise comparisonSpillover effectStability (learning theory)Product (mathematics)EconomicsWelfareMicroeconomicsContrast (vision)Mathematical economicsIndustrial organizationComputer scienceMathematicsMarket economy

Abstract

fetched live from OpenAlex

Abstract This paper considers the Goyal and Moraga‐Gonzalez (2001 ) model of strategic R&D collaboration networks in the open economy framework. The R&D is the d'Aspremont and Jacquemin (1988 ) process innovation and collaboration takes the form of research joint ventures (RJV) in which firms cooperate in R&D but compete in product markets. Countries decide whether to establish free‐trade links while firms decide whether and with whom to form RJVs. A double‐layer pairwise stability concept is introduced to characterize equilibrium network structures. In contrast with conventional wisdom, it is shown that global free trade generally reduces collaborative R&D levels. We give conditions for which pairwise stable R&D networks are welfare maximizing. Stability and efficiency are congruent when R&D cost is either too high or too low. A large public spillover effect is detrimental to an R&D network when trade networks are regional.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.194
GPT teacher head0.399
Teacher spread0.206 · 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