Science Parks: Actors or Reactors? Canadian Science Parks in Their Urban Context
Bibliographic record
Abstract
In response to the current accepted wisdom that we are now in a ‘knowledge economy’, where economic growth is directly linked to the capacity to gather and analyse information, an increasingly popular policy approach has been to foster the development of science parks. These parks, it is argued, contribute to the development of learning regions' by encouraging knowledge transfer between academic institutions and ‘high-tech’ or ‘knowledge-intensive’ establishments, thereby bringing about start-ups and growth in these sectors. Over the last 25 years, 17 such parks have opened in Canada, and in this paper the authors set out to answer two questions. First, what do these parks consist of? Second, can it be shown that they stimulate high-tech employment (whether in the manufacturing or service sectors) in the regions in which they are located? It is found that there is no link between the opening of a science park and employment growth in high-tech sectors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".