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Who is My Partner and How Do We Dance? Technological Collaboration and Patenting Speed in US Biotechnology

2010· article· en· W2167845057 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

VenueBritish Journal of Management · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsIntellectual propertyCompetition (biology)Context (archaeology)BusinessValue (mathematics)Knowledge transferIndustrial organizationProduct (mathematics)PopulationMarketingEconomicsManagementSociologyPolitical scienceLawBiology

Abstract

fetched live from OpenAlex

In settings where patents and intellectual property provide a strong regime of appropriability, the race to be the first firm to patent a product or a process is a central feature of competition. In this context, we hypothesize that cooperative arrangements that only gain access to external knowledge contribute less to heterogeneity between firms and have a much weaker influence on patenting than alliances that transfer highly firm‐specific knowledge, residing in individual and social relationships. We also hypothesize that cooperations between private firms and public organizations accelerate the rate of patenting to a higher degree than cooperations among private firms. We develop and test these ideas on the population of 839 US biotechnology firms between 1973 and 2003. We discuss the importance of our findings on the debate about the value of knowledge access versus knowledge transfer in strategic alliances.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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