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Record W2088744996 · doi:10.1080/10438590410001628378

Innovation quality and manufacturing firms' performance in Canada

2004· article· en· W2088744996 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

VenueEconomics of Innovation and New Technology · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProductivityInnovatorQuality (philosophy)Value (mathematics)Multivariate statisticsEconomicsMarketingMarket shareIndustrial organizationSurvey data collectionBusinessStatisticsEntrepreneurshipEconomic growthMathematics

Abstract

fetched live from OpenAlex

The overall objective of this paper was to determine the impact of producing a world-first innovation, a Canada-first innovation and a first-to-the firm innovation on firms' economic performance (employment, labour productivity, market share and total value added). The study used unique data from Statistics Canada's 1999 Survey of Innovation that was linked to the 1997 Annual Survey of Manufactures. Three hypotheses were tested: that innovative firms (firm-first, Canada-first, world-first) should have higher performance (in terms of the performance measures that are defined in the next section) than non-innovative firms; that the dichotomous innovation variables should be statistically different from zero in the multivariate analysis; that the estimated coefficients in the performance regressions should be greater for world-first innovations compared to firm-first innovations. In the regressions world-first innovators had higher employment and market share offering support for the first hypothesis, while the results for labour productivity and total value added were not statistically significant. With regard to hypothesis two, the multivariate results were somewhat mixed since the world-first innovator was significant in two performance equations. Hypothesis three was confirmed since in all cases the ordering on coefficient size for the performance variables was world, Canada, and firm (with world being the largest and firm being the smallest).

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.225
Teacher spread0.189 · 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