The Expectations of Businesses Settled in a Science Park
Bibliographic record
Abstract
Science parks are created to support the development and growth of knowledge-based businesses and to foster the economic development of a region. Based on an exploratory qualitative study, this research project aims, firstly, to understand the expectations of businesses established in science parks, which is not very well documented in the literature and, secondly, to highlight the motivations of a business to settle in a science park. In order to do so, the research is based on a constructivist approach. Twelve semi-structured interviews were conducted between September 2016 and April 2017 with managers of organizations established in seven science parks in the province of Quebec, Canada. The analysis these interviews identified six major motivations of businesses to settle in a science park. It also revealed eleven major expectations of the science park's contributions for a business established. As a contribution, this research provides some recommendations not only for the managers of science parks, but also for the managers of businesses who want to be established in a science park. This research provides insights for science park managers about the incentives to set up to attract new business and about the support to provide for the business established in a science park in their development.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".