A Four-Step Process to Reposition Small Schools as Sites Within Teaching and Learning Networks
Bibliographic record
Abstract
The international problem facing small high schools in rural communities of providing access to educational and vocational opportunities that approximate those available to urban students has been addressed by repositioning these institutions as sites within teaching and learning networks in the Canadian province of Newfoundland and Labrador. Four inter-connected dimensions of change are outlined (technological, pedagogical, organizational, and conceptual) whereby small rural schools in this Canadian province were repositioned as sites in teaching and learning networks thereby enhancing educational and vocational opportunities for senior students. There are implications in these changes for the professional education of high school teachers who are increasingly likely to be required to teach in networked classes as well as in traditional classrooms.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".