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Record W2601193201

Who controls the development of skills in technology districts? The case of the Quebec Region's IT ecosystem

2015· article· en· W2601193201 on OpenAlexaffabout
Michel Racine, Frédéric Hanin

Bibliographic record

VenueInternational Journal of Entrepreneurship and Innovation Management · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsProcurementGovernment (linguistics)Business ecosystemBusinessFunction (biology)EcosystemDistribution (mathematics)Private sectorDynamics (music)Industrial organizationMarketingEnvironmental resource managementRegional scienceEconomicsEconomic growthKnowledge managementEcologyGeographySociology
DOInot available

Abstract

fetched live from OpenAlex

This article analyses the features and dynamics of the skills ecosystem in the Quebec region's IT industry. The skills-based approach allows links to be made between business and employment relationships by going beyond the organisational level and focusing on the connection between private firms and public organisations. Understanding the institutional foundations of industrial dynamics is both quantitative and qualitative in nature. The study examines the major role of government; firstly as a direct public service employer and, secondly, as an indirect employer by function of the tendering structure that involves the private sector. However, the long-term viability of the ecosystem cannot be guaranteed without the recruitment and integration of immigrant workers, the success of which is based on the dynamics of skills distribution and the content of calls for tender between IT firms and the government. The coordination developed by intermediate organisations is thus central for the regional ecosystem within an industry like IT that is experiencing uncertainty and continuous technological evolution.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.253
Teacher spread0.217 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2015
Admission routes2
Has abstractyes

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