Collaboration and Communication in the Leadership of Educational Technology
Bibliographic record
Abstract
The communication practices between educators, administrators, government officials, and students were key features of educational technology leadership in Western Canada. This paper presents the findings of an exhaustive study of all 75 large K-12 districts in Canada's three westernmost provinces: British Columbia, Alberta, and Saskatchewan. A data transformation model mixed methods triangulation design methodology was used in this study of over 1.1 million students across a geography of over 2.2 million square kilometers. Western Canadian K-12 relies heavily on cloud computing, and this approach enabled a successful shift to online education during COVID-19. What emerged from this research were several organizational collaboration practices that enabled inter-disciplinary communication 1i. These practices produced this highly robust IT infrastructure. This paper will present these collaborative communication practices and the formalized organizational initiatives that enable them. The extent and impact of these interdisciplinary collaborative communication practices were so profound that they nullified differences in school district size and the locus of authority for educational technology decision-making.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".