Firm Demographics in Silicon Valley North
Bibliographic record
Abstract
The Ottawa-Gatineau Commercialization Task Force (CTF)was created to address issues of firm size among technology firms in SiliconValley North (SVN).To aid the CTF, teams from the business schools atCarleton University and the University of Ottawa collected research (1) toobserve the distribution of firms of various sizes in the region, and (2) tocompare SVN to other comparable technology clusters in the world.Focusingon five technological clusters (telecommunications, photonics,microelectronics, software and life sciences), the researchers utilized datacollected by the Ottawa Center for Research and Innovation (OCRI). The five clusters are described, as are the limitations of the OCRIdata.A history of SVN provides a thorough description of the evolution ofthis Canadian technology cluster, with its success attributed to its researchbase and the presence of a large private sector firm.The prevalence ofthe five technological clusters is also examined.SVN's firms are comparedin terms of size and distribution to high-tech firms in Silicon Valley,California, and Oxfordshire, England. The conclusions of the research were twofold:(1) when compared toSilicon Valley and Oxfordshire, Silicon Valley Northhas proportionatelyfewer small high-tech firms and more medium and large firms, and (2) incomparison to large California firms, Canadian high-tech firms have a tendencyto be small. (AKP)
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".