e-learning en entreprise. Un aperçu de l’état des lieux au Canada et au Québec
Bibliographic record
Abstract
La formation en entreprise est devenue une vritable conomie de service. On prvoit que le e-learning sera un leader au niveau de l'conomie mondiale. Dans cet article, nous voyons la place assigne la formation en gnral dans les entreprises, le rle croissant de la formation au Canada, ce qu'est le e-learning, ses avantages et ses inconvnients pour l'entreprise comme pour les employs, le capital humain au Canada, ainsi que les enjeux propres au Qubec. Par la suite, nous faisons tat d'une recherche mene auprs de dix entreprises du Qubec sur la place de la formation et le e-learning selon les responsables de formation, les DRH et les formateurs. ABSTRACT. Corporate training has become a service-based market. Forecasts predict that E-Learning will become a leader in the new global economy. In this article, we will examine the role of general training in corporate settings, training in Canada and we will define E-Learning, compare its advantages and disadvantages for the company and the workers, and human capital in Canada and the challenges specific to Quebec. We will also present a research that was conducted in ten Quebec corporations on the role of training and E-Learning in the eyes of training coordinators, human resources directors and trainers.
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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 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".