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Record W3038910930 · doi:10.1522/revueot.v29n2.1150

Le coworking en région au Québec : une innovation territoriale et entrepreneuriale contribuant au développement local?

2020· article· fr· W3038910930 on OpenAlexaffvenueabout
Diane‐Gabrielle Tremblay, Erwan Vaineau

Bibliographic record

VenueRevue Organisations & territoires · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Les espaces de coworking sont apparus d’abord dans les grandes métropoles du monde, mais se sont aussi multipliés en région. Ils visent l’échange de connaissances, la collaboration, le réseautage et l’innovation territoriale. Alors que ces espaces ont fait l’objet d’études dans les espaces métropolitains, ils ont été très peu étudiés en région. Nous avons donc mené une recherche dans des espaces de coworking en région au Québec. Nous avons pu constater que ces espaces visent souvent les mêmes objectifs de collaboration et de réseautage, mais qu’ils s’en distinguent aussi en région : les coworkers sont un peu plus âgés en moyenne et ont souvent une orientation plus entrepreneuriale, bien qu’ils soient aussi souvent travailleurs autonomes. Les espaces sont moins nombreux en région et moins axés sur les services d’animation ou la différenciation par la décoration ou l’ambiance. Les coworkers en région se préoccupent davantage de la dimension entrepreneuriale et gestionnaire, de services d’affaires et aussi de leur contribution au développement territorial local.

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.008
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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

Citations4
Published2020
Admission routes3
Has abstractyes

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