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Record W4246549027 · doi:10.5912/jcb246

A comparison of R&D indicators for the Vancouver biotechnology cluster

2008· article· en· W4246549027 on OpenAlexafffundabout
Mónica Salazar Villanea, Martin Bliemel, J. Adam Holbrook

Bibliographic record

VenueJournal of Commercial Biotechnology · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsKingston Health Sciences CentreAdministrative Sciences Association of CanadaSimon Fraser University
FundersIndustry CanadaGovernment of CanadaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsRevenueInvestor relationsIntellectual propertyBiotechnologyCluster (spacecraft)Value (mathematics)Variety (cybernetics)BusinessBioethicsEconomicsMarketingBiologyStrategic managementPolitical scienceAccountingLawComputer science

Abstract

fetched live from OpenAlex

The basis of this paper is to go beyond abstract definitions of what a cluster is, and look at a variety of measurable indicators, to see which can demonstrate the presence of a cluster. The example presented is based on the biotechnology industry in Vancouver, Canada. Biotechnology differs from conventional industries, in that there are few tangible goods or services traded, but rather the basis of value creation is primarily the sale or licensing of intangible intellectual property or the (usually pre-revenue) firms themselves. The two main questions we aim to test are (i) is there a biotechnology cluster in Vancouver, and (ii) what are its inputs, outcomes, and impact on the region? We use data provided from local and federal agencies such as LifeSciences British Columbia and Statistics Canada to compare biotechnology R&D activity across regions, and within the local economy. Our findings indicate that there is significant activity around biotechnology R&D and commercialisation in Vancouver, but no guarantee of the longevity of the innovation system.

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 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 score0.792
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.081
GPT teacher head0.314
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2008
Admission routes3
Has abstractyes

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