Applications Development for Education: Using Communications Satellites
Bibliographic record
Abstract
This paper describes the introduction of the educational applications of satellites in Canada. It includes an overview of the field trials, a discussion of the role of the Department of Communications (DOC) Co-ordinators, and comments on a number of issues and considerations taken into account during the experimental period. The author was a key player in the field trials and bases the paper on his knowledge, as a primary source, of the process of the transfer of technology. He notes that the success of the adoption of satellite applications by educators was influenced, in large measure, by the field trial process, the role of the DOC Co-ordinators, federal provincial partnerships, and the willingness of the educational community to innovate. Le present article decrit l'introduction au Canada des applications pedagogiques des communications par satellite. Il presente un survol des essais en milieu reel, se penche sur le role des coordonnateurs et coordonnatrices du Ministere des Communications et commente certaines questions et problematiques reliees a la periode experimentale. L'auteur a joue un role cle lors des essais en service et est donc en mesure de presenter un temoignage de premiere main sur le processus de transfert des techniques. Il souligne que le succes de l'adoption de ces applications dans le milieu enseignant a ete dans une large mesure tributaire du processus d'essais en milieu reel, du role des coordonnateurs et coordonnatrices du ministere, du partenariat federal-provincial, et du desir d'innover manifeste par la communaute educative.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".