Catching the bus: A call for critical geographies of education
Bibliographic record
Abstract
Abstract Informed by recent struggles over schooling, this article proceeds from the premise that education is a deeply geographic and urgently political problem increasingly engaged by a wide range of scholars and activists. We argue that the current political moment demands increasing geographic attention to the confluence of social processes that shape schooling arrangements. We contend that this attention also must address how people involved in collective action understand and enact alternatives and how these mobilizations may articulate with other social movements. Although existing geographers of education have studied schooling in relation to other processes such as gentrification and citizenship, we argue that centering schooling as an object of study can enliven important disciplinary conversations. In light of these arguments, we call on geographers to advance geographic scholarship on education by creating a cohesive critical geographies of education subfield. Drawing from intensified interest in the geographies of education, this subfield can contribute to broader geographic debates by centering schooling in theory generation, rather than only studying education as a site of test cases for existing geographic theories. Given this call, this review highlights how the existing literature on schooling signals the potential of geographic work on education and marks considerations for the development of future research.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.008 | 0.101 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".