Regional Systems of Innovation in Canada: Two Case Studies
Bibliographic record
Abstract
The need for the development of regional innovation systems is widespread and recognised in many countries. Quite a few studies have examined individual clusters with the purpose of identifying their essential components and of determining the conditions in which they may arise and prosper. In most of the literature however, it is recognized that there is little firm knowledge about their development process, and about the ways in which they can be promoted and fostered. The "path specificity" of their developments seems to hinder attempts at identifying specific measures that can promote their appearance and growth in contexts different from those where they already have. Canada's effort in this endeavour, however, seems to be paying off, since the country can boast of several burgeoning innovation clusters, some of which seem to be responding to specific policy measures at the regional level. In this study we have aimed at elucidating the structure and dynamics of two innovation clusters in British Columbia: the biotechnology innovation cluster, and the fuel cell innovation cluster, in order to gain a better understanding of their structure, their dynamics, and the way in which they respond to specific efforts by their actors and promoters
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".