Les études supérieures en ligne pour les leaders : Le cas des officiers des Forces canadiennes
Bibliographic record
Abstract
This paper argues in favour of distance education in social sciences at the masters level for Canadian Forces officers. First, it demonstrates how distance learning has played a substantial role from the very beginning of university teaching (with Plato’s Academy). It then tries to demonstrate how technology will be central to the pedagogy of educational institutions in the future. Finally, it explains why Canadian Forces officers need to pursue higher education in social sciences even after they have undergraduate degrees, and how the nature of “3D” missions (for Defence, Diplomacy and Development) requires a more thorough understanding of social sciences. The author then suggests a meaningful way to realize this objective. The BOLD (Blended Online Learning Design) initiative demonstrates many of the characteristics required for this type of training: the possibility of studying entirely at a distance, a hybrid of synchronous and asynchronous learning, recognition of the teaching corps needs, transnational and international careers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".