Bibliographic record
Abstract
Firms in spatial concentrations of advanced-technology activities do not constrain their knowledge inputs to opportunities found within their industrial cluster. Rather, firms seeking extra-regional markets augment their in-house resources by means of material (embodied technology) and knowledge inputs obtained from sources at the extra-regional scale in addition to within the region. Literature is reviewed on the clustering of firms and their network geography, models of open and closed industrial systems, and absorptive capacity. The latter is used to interpret the search for knowledge undertaken by firms, which are discussed in terms of their organizational differences and strategic choices. A sample of manufacturing establishments from the electronics cluster in the Toronto metropolitan region (Canada) shows firms that are export-intensive have significantly stronger international input connections, especially with consultants and alliance partners. Export orientation is associated with higher levels of expenditure on the in-house generation of knowledge, more innovation inputs from external sources, and distinctive network geographies. Differences in network relations occur within and between three organizational groups of firms – foreign affiliates, multi-location and single-location domestic firms. Geographically wide-ranging networks are interpreted in terms of opportunities in extra-regional locations compared with local supplies. Regional innovation policy implications are considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".