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Record W2015510410 · doi:10.1080/09654310902949240

A Comparative Study of the Aquaculture Innovation Systems in Quebec's Coastal Region and Norway

2009· article· en· W2015510410 on OpenAlexaffabout
David Doloreux, Arne Isaksen, Heidi Wiig Aslesen, Yannik Melançon

Bibliographic record

VenueEuropean Planning Studies · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversité du Québec à RimouskiUniversity of Ottawa
Fundersnot available
KeywordsAquacultureNorwegianBusinessInnovation systemRegional developmentEconomic geographyRegional scienceGeographyEnvironmental planningFisheryFish <Actinopterygii>Industrial organization

Abstract

fetched live from OpenAlex

This paper examines the actors and activities and the institutional–spatial dynamics that characterize innovation and knowledge processes within the aquaculture industry and its support organizations in the coastal region of Quebec, Canada. It aims to identify the main features and components of such support organizations and their roles in entrepreneurial and knowledge processes. Comparing this Canadian case with the more developed Norwegian innovation system in aquaculture, the paper concludes that the market possibilities for the products of aquaculture are almost the same in Norway and Quebec. However, it is the policy and institutional settings, as well as the historical trajectories of the respective innovation systems, which seem to explain the growth of the aquaculture industry in Norway and its less successful development in Quebec. The paper also investigates the conditions and institutional arrangements that may stimulate the building and development of a more mature aquaculture innovation system support in Quebec's coastal region.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.407

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.278
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations18
Published2009
Admission routes2
Has abstractyes

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