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Networks of knowledge among unionized firms

2008· article· en· W1892011355 on OpenAlexvenueno aff
Ana Mauleón, José J. Sempere‐Monerris, Vincent Vannetelbosch

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOligopolyBargaining powerOrder (exchange)Pareto principleMicroeconomicsNetwork effectPareto optimalStability (learning theory)Power (physics)EconomicsBusinessIndustrial organizationLabour economicsComputer scienceSet (abstract data type)Operations management

Abstract

fetched live from OpenAlex

Abstract. We develop a model of strategic networks in order to analyze how trade unions will affect the stability of R&D networks through which knowledge is transmitted in an oligopolistic industry. Whenever firms settle wages, the partially connected network is likely to emerge in the long run if and only if knowledge spillovers are large enough. However, when unions settle wages, the complete network is the unique stable network. In other words, the stronger the union bargaining power is, the more symmetric stable R&D networks will be. In terms of network efficiency, the partially connected network (when firms settle wages) does not Pareto dominate the complete network (when unions settle wages) and vice versa.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.116
GPT teacher head0.171
Teacher spread0.055 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2008
Admission routes1
Has abstractyes

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