A spatial analysis of small‐ and medium‐sized information technology firms in Canada and the importance of local connections to institutions of higher education
Bibliographic record
Abstract
As part of community/regional development policy, governments in Canada attempt to create conditions that stimulate the formation of high‐technology clusters and, in so doing, often encourage firm–university/college liaisons. Information technology (IT) is an important segment of the high‐technology sector, and small‐ and medium‐sized Canadian IT firms are disproportionately attracted to large metropolitan areas (with Toronto, Ottawa–Hull, Vancouver, Calgary, Montréal, Kitchener and Edmonton being the most noteworthy). A nearest neighbour analysis suggests that small‐ and medium‐sized IT firms are clustered within the metropolitan setting, and a nearest neighbour hierarchical spatial clustering technique demonstrates that intra‐urban IT agglomerations can be objectively identified. The linkages between small‐ and medium‐sized IT firms and higher education institutions are, on average, not strongly entrenched within Canada's IT culture, although many of these firms still connect with universities or colleges through co‐operative programs and other means of employee recruitment and via general networking with faculty members. Thus, governments may be able to support IT cluster formation by encouraging firm–university/college connections that centre on student participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".