The incommensurability of digital and climate change priorities in schooling: An infrastructural analysis and implications for education governance
Bibliographic record
Abstract
This paper introduces the concept of infrastructure into discussions on climate change and education. We focus on the links between the increased use of digital data and the central role of data infrastructures in education, and the energy infrastructure needed to support their growing use in schools and school systems. We elaborate a need for a greater accounting of the climate and related social costs of these interwoven digital and energy infrastructures of schooling. We suggest this is part of the 'disposition' of the infrastructures of schooling that should be weighed into decisions on whether and how to continue with digital technologies in schools. By acknowledging the climate and environmental incommensurability of digital infrastructures, education leaders and young people can more fully understand their dispositions and their costs. We propose three implications for education governance that entail greater consideration of the limits of current school climate change infrastructures such as 'eco school' programs and EdTech 'AI for good' initiatives, pushes for 'computing within limits' without substantial changes, and current school governance practices which unnecessarily rely on digital infrastructures. Instead, what is needed may be a reversal of the extensive use of digital infrastructures by schools and education governance bodies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".