Bibliographic record
Abstract
A VLE can be understood as a computer-accessible cognitive context where students interact with mediated representations rather than their experiential equivalent. For students of school age, an emergent variation is the Virtual School, where the internet is used to deliver some or all of the courses that would traditionally be offered in a “bricks-and-mortar” school in face-to-face mode with a teacher. Increasingly, Virtual Schools are an available alternative to conventional schools. Unlike their physical counterparts, Virtual Schools are, for the most part, not restricted by the restraints of timetables, bells, uniforms, or other organizational characteristics of face-to-face teaching. Virtual Schools have shown considerable growth in recent years, particularly in the U.S.A. (Clark, 2001) and Canada (SAEE, 2002). They are seen as having identifiable advantages. Mittleman (2001) refers to a Virtual School in Israel as “breaking barriers of time and place” (p. 84), while Florida High School in the U.S.A. uses the motto “any time, any place, any path, any place” (Florida Virtual School, 2003). Virtual Schools have been described in quite glowing terms. Berman (1999) sees virtual learning in the following way:
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 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".