Organizing Distance Learning in the North; Analysing Canadian K-12 Policies for Distance Learning
Bibliographic record
Abstract
To explore what role distance learning may play in strengthening human capital in the Circumpolar North, this case study describes the development of distance learning policies on organization and facilitation in K-12 education in Canada and suggests explanations for this development. Because the decentralized structure of Canadian governance and the autonomy of the provinces and territories impact policy development in the education sector, distance learning policies can develop differently across the country. In the provinces where the student population is large enough to support distance education, several providers of distance education operate in the same market, but in some jurisdictions with smaller student populations, there are only one or two programs. Despite differences, there are also some significant similarities in how distance learning is facilitated as a learning environment in different jurisdictions. Although policy changes in the Canadian education sector often take place incrementally, there are examples of radical shifts as well, which tend to generate political debate. Investigating both governance aspects of distance learning and the facilitation of educational technology for instructional design, the thesis suggests some elements of the Canadian experience that are valuable for other areas in the rural North.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".