The Techification of Education in Ontario's virtual schools
Bibliographic record
Abstract
In Ontario, Canada, the COVID-19 pandemic prompted the creation of publicly funded virtual schools for K-12, synchronous remote learning. Going into the 3rd year of operations, many of these schools are transitioning into permanent learning options. In this paper, I present preliminary findings from my doctoral research examining principals’ leadership practices in these virtual schools. Qualitative interviews conducted in Spring 2022 reveal an emerging trend towards the techification of education: A phenomenon wherein Big Tech becomes enmeshed in all parts of education. Results show that virtual schools are increasingly relying on Google/Alphabet products in ways that may place schools as training grounds for lifetime consumer loyalty and may exacerbate existing inequities. I investigate this problem through the lens of school principals, as principals are a mediating point between policy and practice. Finally, I offer suggestions for how to mediate the techification of education at both the principal and policy level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".