New Systemic Roles Facilitating the Integration of Face-to-Face and Virtual Learning
Bibliographic record
Abstract
The introduction of web-based education in Canadian schools, as in other developed countries, has been particularly noticeable in rural areas. Small schools in rural communities have continued to get smaller as families relocate in urban areas in search of increased educational and vocational opportunities. There are a number of issues common to the technological enhancement of rural schools: the building of appropriate infrastructures, the implementation of information and communication technologies and the development of pedagogy for web-based education. In the Canadian province of Newfoundland and Labrador a number of new positions and roles have been created to distribute responsibilities for these issues and to ensure the success of virtual schooling. Newfoundland and Labrador Newfoundland and Labrador is the eastern-most province of Canada and is situated on the North Atlantic Ocean west of Greenland and north of the United States. The province consists of a large, roughly triangular island (108,600 square kilometers), an irregularly-shaped mainland territory (294,300 square kilometers), and over seven thousand smaller islands (3,598 square kilometers). The climate ranges from temperate to arctic and the terrain ranges from gently rolling hills to mountainous. The major exports of the province are iron ore, petroleum, hydropower, fish, and people. Historically, the fishing industries drew people to Newfoundland and Labrador from Great Britain, France, Portugal and Spain; however, the trend in the 20th century was out-migration to other Canadian provinces and the United States in search of employment. Recently, there has also been a significant population migration inside the province to larger communities and especially to St. John’s, the capital city, which has a metropolitan population of approximately 200,000. Education in Rural Newfoundland and Labrador In Newfoundland and Labrador the introduction of web-based education is centered on small schools in rural communities for whom recent technological developments offer not just new teaching and learning opportunities but a life-line for their continued existence. Meeting the needs of students located in rural communities has required special consideration in the development of new, electronic educational structures.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".