Stretching spatial theories in comparative education: new approaches for challenging times
Bibliographic record
Abstract
The aim of this article is to stretch spatial theorising in the field of comparative education. Among the different spatial theoretical approaches that have been explored in educational research in the last 10 years, we review social topology, spatial-temporalities, and beyond-human spatialities and how they have been used in comparative education research. To illustrate the potential of these approaches we then use them to analyse recent technological changes, shifts in governance, the impacts of global crises and the complicated ways in which they are spatially related to education. Through our analysis, we argue that comparative education research would benefit from making space for spatial theorising in relation to time, materiality, and the beyond-human, not only to better understand the world, but also to consider how education is ethically linked to the existential challenges humanity is facing today.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.008 | 0.092 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".