MétaCan
Menu
Back to cohort
Record W2133136109 · doi:10.3152/147154406781775913

Research funding by city: an indicator of regional technological competitiveness?

2006· article· en· W2133136109 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueResearch Evaluation · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsSimon Fraser University
FundersAustralian Government
KeywordsAgency (philosophy)Intellectual propertyInvestment (military)ProductivityBusinessInformation and Communications TechnologyPopulationRegional scienceFunding AgencyEconomic growthEconomic geographyEconomicsPolitical scienceGeographyPublic relationsSociologySocial science

Abstract

fetched live from OpenAlex

The chief funders of university research in Canada distribute their funds through a competitive peer-review system. We calculated the research investments by each agency in 27 distinct regional districts/cities of Canada, and plotted expenditures against the proportion of highly qualified persons in the population, as an indicator of its receptor capacity. These ratios should be a good indicator of the ‘productivity’ of the region in terms of intellectual property. The existence of several, globally competitive, clusters in Canada is well-documented and clear linkages to university research have been traced through studies of licensing and spin-off activities. The biotechnology and information and communication technology (ICT) sectors are examples of areas where there appear to be clear links between granting agency investment and industrial activity.

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)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.544
GPT teacher head0.488
Teacher spread0.056 · 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