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Mapping Regional and Sectoral Characteristics of Knowledge‐Intensive Business Services: Evidence from the Province of Quebec (Canada)

2008· article· en· W2108015392 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

VenueGrowth and Change · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsBusinessEconomic geographyResource (disambiguation)Scale (ratio)Regional scienceGeographyCartographyComputer science

Abstract

fetched live from OpenAlex

ABSTRACT The study presents original evidence on the characteristic features and innovation activities of knowledge‐intensive business services (KIBS). Based on a wide‐scale survey of 1,124 KIBS firms in Quebec (Canada), we explore empirically the extent to which KIBS from various sectors and regions differ in their characteristics and their uses of innovation practices. The results from the sectoral analysis reveal that KIBS display different characteristic features and innovation behaviours across sectors, thus suggesting that inter‐sectoral differences are important when explaining innovation activities in KIBS. The comparison between KIBS in large, medium, central, and resource regions shows that the characteristic features and the innovativeness of KIBS are rather similar, and little or no significant statistical differences were found between the different regions in the province of Quebec. Thus, overall, the results of our study seem to suggest that a location does not tend to make a difference in respect to characteristic features and innovation performance of KIBS.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.213
Teacher spread0.163 · 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