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Record W2092609197 · doi:10.1080/09654311003593986

Demographics, Innovative Outputs and Alliance Strategies of Canadian Biotech Firms

2010· article· en· W2092609197 on OpenAlexaboutno aff
Yael Levitte, Sharmistha Bagchi‐Sen

Bibliographic record

VenueEuropean Planning Studies · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationAllianceAbsorptive capacityDemographicsValue (mathematics)BusinessSample (material)MarketingBiotechnologyIndustrial organizationPolitical scienceBiologySociology

Abstract

fetched live from OpenAlex

This paper focuses on the characteristics of biotech firms that consider alliances as critical to the innovation and commercialization of biotech-based products. First, we consider alliances with both universities and industries. Next, we examine attributes for those firms who consider proximity to universities as critical compared with others that do not put high value on physical proximity. Our study is informed by the literature on the biotechnology industry as well as studies on absorptive capacity, alliances and clusters in exploration and exploitation of knowledge, research and technologies. We analyse data based on a 2002 survey of Canadian biotech firms and find that while collaborative arrangements with universities are the most common among our sample firms, those who assign a high value to such linkages are not necessarily always the biotech firms experiencing commercial success.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.272
Teacher spread0.227 · 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

Citations11
Published2010
Admission routes1
Has abstractyes

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