Scalar dimensions of non-market governance in knowledge economies: A look at the microelectronics industry in the Greater Toronto Region
Bibliographic record
Abstract
This paper examines the non-market governance processes that have strategically supported and shaped the microelectronics industry in the Greater Toronto Area (GTA). With focus on governance actors from each level of the state from the early stages of the industry’s development, the analysis shows how the federal level, once central to early strategic investments, has become increasingly less of a participant. Such a finding is in keeping with the claim by Swyngedouw (2003) and others, that advanced economies are experience a rescaling as a result of knowledge intensification whereby the networks involved in coordinating the economy are less dominated by the national level and increasingly animated by actors and institutions at the local and regional levels. Within the GTA, however, strategic economic coordination at the local level has been far from coherent, leaving a void in multilevel governance pattern that supports this important industry. There is some indication that this may be changing. In recognition of the importance of local level in localizing strategic investments, creating institutional supports for firm creation and growth, and in shaping the socio-economic environment, several new actors have emerged recently with locally focused strategic intentions. Given the many institutional barriers within the GTA, both cultural and political, it is far from clear, however, whether such developments will ever transpire into an integrated ‘economic community ’ capable of responding continually to the restlessness of knowledge intensive industries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".