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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".