An Innovative Model for Skill Development in Silicon Valley North: O-Vitesse
Bibliographic record
Abstract
A complication for many high-technology firms is alack of skilled workers.Firms in Silicon Valley North face hiringdifficulties due to a lack of well-trained university graduates and to theinflux of immigrants lacking the skills necessary to perform in Canada'shigh-technology industry. Three key factors highlight the need for the promotion of improved skillsamong technology employees.As knowledge-based economies continue toincrease, the need for skilled, well-educated workers alsoincreases.Additionally, the need for continuous employee learning andtraining is significant due to the increase in knowledge-basedeconomies.However, the predicted slow growth of Canada's labor force, aswell as declines in student enrollment in technology-based disciplines, maynegatively impact upon Canada's ability to compete with other knowledge-basedeconomies.The role of skilled immigrants in Canada is examined. Since training is necessary for technology employees to remain on thecutting-edge of research and development, the Canadian Regional InnovationForum (NRC) proposed a pilot program called O-Vitesse (Ottawa Venture inTraining Engineers and Scientists for Software Engineering).The historyof O-Vitesse, the growth of the program, and the impact of the program onCanada's technology industry are discussed.O-Vitesse is deemed asuccessful, inexpensive tool for Canadian technology firms. (AKP)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".