Book Review - Development and Management of Virtual Schools: Issues and trends. Editor Catherine Cavanaugh
Bibliographic record
Abstract
Within Canada, the virtual school movement is growing.Contact North, the virtual school serving Northern Ontario, reported 11,222 registrations in their 548 courses for the year 2000-01 (Betty, Hebert, and Sefton, 2002).In British Columbia, a partnership of eighteen school districts offered a pilot electronic distance education program for 2,200 students in 2001-02, while the Vancouver School Board (that province's largest school board) was in the process of setting up its own virtual school (Kuehn, 2002).Over the past four years, the Centre for Distance Learning and Innovation in Newfoundland and Labrador has increased its offerings from 200 student enrolments from 76 different schools in ten courses in 2001-02 to 1,500 student enrolments from 95 different schools in thirty-five courses in 2004-05 (Government of Newfoundland, 2004).From 1995 to 1999, there were "23 online programs [that] have begun operating in the province [of Alberta]" (Muirhead, 1999)."Alberta leads the [virtual schooling] pack with about 4,500 fulltime and 2,500 part-time K-12 students in online schooling -over 20 schools offer online courses" (O'Haire, Froese-Germain and Lane-De Baie, 2003, p. 6).However, there has yet to be a book published that has really addressed the virtual school movement across Canada.Cavanaugh's Development and Management of Virtual Schools: Issues and Trends is the first step in addressing that deficiency.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".