Bibliographic record
Abstract
The history of online learning at the K-12 level is almost as long as its history at the post-secondary level, with the first virtual school programs beginning in the early 1990s. While these opportunities were designed as a way to provide rural students with access to more specialized courses, as opportunities have become organized into virtual or cyber schools the nature of students served by these institutions have broadened. Unlike online learning in general, much less is known about virtual schooling – even less of which is based on systematic research. Regardless, the growth and practice of virtual schooling has far out-paced the production of reliable and valid research. This paper will focus upon describing the evolution of K-12 online learning in Canada and the United States, how that evolution has impacted rural schools, and what lessons can be learned from the experiences with K-12 online learning in these two countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".