Any Time, Any Place, Any Way, Any Pace: Markets, EdTech, and the spaces of schooling
Bibliographic record
Abstract
For decades investors have sought to find ways of profiting off the billions of public dollars spent annually on systems of public schooling across the world. This interest has coincided with the growing marketization of systems of public schooling, especially in the United States, as well as the increased use of educational technologies (or EdTech). This study examines the implications of the growing use of profit-driven educational technologies for the politics and spatial practices of schooling. Specifically, it examines past experiences with market-oriented EdTech systems in Oregon and Michigan to highlight how the combination of market systems of governance and profit-driven EdTech practices depend on the deconstruction of links between schools, communities, and students in order to roll out aspatial and apolitical educational practices that maximize profits. The placeless vision for education embedded in profit-driven EdTech helps promote the reproduction of dominant orders and stifles place-based struggles over educational justice.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".