School Choice Research and Politics with Pierre Bourdieu: New Possibilities
Bibliographic record
Abstract
Various sociological perspectives have been applied to facilitate school choice research over the past two decades, as showcased in this 2020 Yearbook of Politics of Education Association. Among them, Pierre Bourdieu’s concepts and theories stand out as a catalyst for the field’s sociological development. My first objective in this article is, thus, to assess the contributions of Bourdieu’s sociological theory to school choice scholarship to date. I review the established and emerging research studies to highlight the significance of Bourdieu’s conceptual system in illuminating the social dynamics of school choice. My second objective in this article is to discuss how Bourdieu’s geographical concerns and concepts have been underutilized in the field. Ultimately, I argue that Bourdieu’s sociospatial concepts can unlock new areas of research and politics by elucidating why and how school choice functions as a mechanism that accentuates social inequality, which is reproduced geographically.
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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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".