Education governance and the infrastructures of schooling: the making of an eco-school in rural Argentina
Bibliographic record
Abstract
This article draws on infrastructure studies to examine how a newly built eco-school in rural Argentina furthers sustainability learning and action. We consider how infrastructures mediate the possibilities of schooling, including the physical building of the school itself and the region in which it is located, the objects, desks, technologies and so on within the school, and broader policy regimes through which education systems are constituted. Drawing on qualitative data, our analysis shows how the physical building of the rebuilt eco-school blurs divisions between the built and natural environment, how the building fosters particular affective and learning practices linked to sustainability, as well as how it is in tension at times with broader regimes of education governance. Amid growing recognition of the need to mobilise education to foster sustainability, we argue that greater attention to the infrastructures of schooling can offer both practical and theoretical contributions in considering education policy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".