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Record W2045335471 · doi:10.1080/00343404.2011.626400

Developing a Knowledge Infrastructure to Foster Regional Innovation in the Periphery: A Study from Quebec's Coastal Region in Canada

2011· article· en· W2045335471 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

VenueRegional Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsWilfrid Laurier UniversityUniversity of OttawaUniversité du Québec à Rimouski
Fundersnot available
KeywordsRegional innovation systemRegional developmentRegional sciencePlan (archaeology)BusinessRelevance (law)Regional planningRegional studiesInnovation systemEconomic geographyGeographyPolitical scienceUrban planningIndustrial organization

Abstract

fetched live from OpenAlex

Melançon Y. and Doloreux D. Developing a knowledge infrastructure to foster regional innovation in the periphery: a study from Quebec's coastal region in Canada, Regional Studies. Building on the case study of Quebec's coastal region maritime industry, the relevance of the regional innovation system framework to analyse and plan innovation development in the periphery is discussed. The analysis indicates that in Quebec's coastal region, while public policies using the regional innovation system framework have contributed to create a relatively well-developed knowledge infrastructure in the maritime industry, they have not yet succeeded in achieving the main goal of fostering a ‘competitive regional production system’. This case suggests that a ‘thickening’ of the knowledge infrastructure does not automatically lead to significant development in the productive system in peripheral regions.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.172
GPT teacher head0.353
Teacher spread0.181 · 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