Training and the competitiveness of the Québec multimedia-IT sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it