Who controls the development of skills in technology districts? The case of the Quebec Region's IT ecosystem
Bibliographic record
Abstract
This article analyses the features and dynamics of the skills ecosystem in the Quebec region's IT industry. The skills-based approach allows links to be made between business and employment relationships by going beyond the organisational level and focusing on the connection between private firms and public organisations. Understanding the institutional foundations of industrial dynamics is both quantitative and qualitative in nature. The study examines the major role of government; firstly as a direct public service employer and, secondly, as an indirect employer by function of the tendering structure that involves the private sector. However, the long-term viability of the ecosystem cannot be guaranteed without the recruitment and integration of immigrant workers, the success of which is based on the dynamics of skills distribution and the content of calls for tender between IT firms and the government. The coordination developed by intermediate organisations is thus central for the regional ecosystem within an industry like IT that is experiencing uncertainty and continuous technological evolution.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".