Bibliographic record
Abstract
This paper overviews a multi-mode research study of Internet Protocol (IP) videoconferencing applications in five early adopter school divisions in Alberta, an oil-rich province located in Western Canada. The applications exploited the affordances of the Alberta SuperNet, a new, very high speed digital network. The study included site visits, surveys, structured classroom observations, and research interventions. The study revealed effective use of videoconferencing for enrichment, professional development, and administration, with less successful application as the primary tool for distance education delivery. I conclude the paper with a discussion of my own reflections on the use of videoconferencing and its capacity to serve as a ‘killer app” for distance education. Résumé Cet article fait un survol d’une recherche multimodale d’applications de vidéoconférence par protocole IP dans cinq districts scolaire avant-gardistes en Alberta, une riche province productrice de pétrole dans l’Ouest canadien. Les applications exploitent les capacités du Alberta SuperNet, un nouveau réseau numérique à très haute vitesse. L’étude impliquait des visites sur les sites, des sondages, des observations structurées en classe, et des interventions de recherche. L’étude révèle une utilisation effective de la vidéoconférence pour l’enrichissement, le développement professionnel et l’administration, et un succès moins grand lorsqu’utilisé comme outil principal dans la diffusion de l’éducation à distance. En conclusion, je présente une discussion de mes réflexions sur l’utilisation de la vidéoconférence et sa capacité à servir de « killer app » pour l’éducation à distance.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".