Training and the competitiveness of the Québec multimedia-IT sector
Bibliographic record
Abstract
This article studies the hypothesis that training is essential to contribute to the competitiveness of the Quebec multimedia-IT sector. We also hypothesised that intermediary organisations and associations contribute to this development of training and competitiveness. The research is based on 30 interviews (15 firms and 15 non-business) in seven different sub-sectors of the multimedia-IT ecosystem, with 11 different types of organisations, in order to determine to what extent training and development of competencies are adequate and do effectively contribute to the competitiveness of the sector. Based on these interviews, we conducted a SWOT analysis of training in the Quebec multimedia-IT sector. This article focuses on the quality of training, diversity of competencies and highlights the challenges in training for firms and non-business organisations, as reported by the interviewees. We conclude that while there are good quality training programs, there are some elements related to entrepreneurship and business issues that are lacking. An increased diversity of workers would be important and integrating more women and foreign workers could help for this.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".