DISTANCE EDUCATION IN CANADA: BEGINNINGS, EARLY AND RECENT DEVELOPMENTS
Bibliographic record
Abstract
The COVID-19 pandemic has had a huge impact on educational systems worldwide, leading to the near-total closures of schools, universities and colleges. Most governments decided to temporarily close educational institutions to reduce the spread of Coronavirus. Millions of students are having their education disrupted. Efforts to slow it through non-pharmaceutical interventions and preventive measures such as social distancing and self-isolation have prompted the widespread closure of primary, secondary, and tertiary schooling in over 100 countries. In the sphere of education, many of the measures that countries have adopted in response to the crisis are related to the suspension of face-to-face classes at all levels, which has given rise to three main areas of action: the deployment of distance learning modalities through a variety of formals and platforms (which or without the use of technology); the support and mobilization of education personnel and communities; and concern for the health and overall well-being of students. In the article developments of education in Canada are considered. The measures taken by the country are the same as in other countries of the world, but the development and implementation of governmental and educational programs for every level and their interaction between institutions, students and their parents are accentuated there. The government cooperates with various organizations (governmental and nongovernmental) trying to provide schools, universities and colleges with all necessary for distance learning, and, firstly, access to modern technology and the Internet. Distance education as a form of learning appeared long ago. But nowadays it has a new challenge to be activated mandatory, other than traditional methods of gaining knowledge should be implemented involving advanced technology.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".