Explorer l’accompagnement nécessaire à la poursuite d’un projet entrepreneurial en région éloignée
Bibliographic record
Abstract
L’entrepreneuriat est vu par les régions éloignées comme une manière de diversifier leur économie afinde se sortir d’une vision axée sur l’exploitation des ressources et de la main-d’oeuvre par des industries dont lesintérêts sont extérieurs et dont l’activité contribue à accentuer les disparités socioéconomiques locales. Malgrécertaines initiatives de soutien publiques et privées, plusieurs contraintes limitent le soutien donné aux entrepreneurs. Le but de cette étude est donc de mieux comprendre l’accompagnement nécessaire à la poursuite d’un projet entrepreneurial dans la région du Saguenay–Lac-Saint-Jean. Pour ce faire, nous utilisons les différents attributs composant l’écosystème entrepreneurial de Spigel (2017) pour analyser et interpréter des entretiens que nous avons réalisés avec différents groupes d’entrepreneurs de la région. Il en ressort entre autres que l’offre d’accompagnement n’est pas toujours adaptée aux besoins des entrepreneurs et qu’il reste des attributs de l’écosystème entrepreneurial à développer. Entrepreneurship in remote areas is viewed as a way to diversify their economies. The objective is to move away from a vision centred on resource and workforce exploitation by industries with outside interests and whose activities contribute to increase local socioeconomic inequalities. Despite some public and private support initiatives for entrepreneurs, a number of constraints limit this support. This study aims to better understand the support required to pursue an entrepreneurial project in the Saguenay–Lac-Saint-Jean region. To achieve this, we use the different attributes making up the Spigel (2017) entrepreneurial ecosystem to analyze and interpret our interviews with various groups of entrepreneurs in the region. This shows, among other findings, that the support offered is not always suited to the needs of the region’s entrepreneurs and that there are attributes of the entrepreneurial ecosystem that remain to be developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".