Culture and Private School Choice
Bibliographic record
Abstract
In sociology, the role of education in cultural reproduction and, by extension, in the reproduction of class-based inequalities has been widely examined. School choice scholars have drawn on work exploring differences in cultural resources (e.g., capital) and comportments (e.g., habitus, concerted cultivation) to highlight the role of school choice in cultural reproduction. However, focus has generally been on differences between choosers and non-choosers, and on the choice between public school and other schooling options. As such, little is known regarding how, once they have decided to engage in private school choice, parents go about selecting specific schools. Moreover, while much attention has been given to practical and academic considerations in shaping parents’ school preferences, the role of cultural factors remains underexamined. When culture is considered, it is generally to examine class-based differences in resources and dispositions between choosers and non-choosers, neglecting potential differences among those engaging in choice. Finally, although parents engaging in school choice ultimately choose among schools to which their children are granted admission, little is known regarding schools’ selection of students/families and if cultural processes linked to homophily and cultural matching identified in other domains factor into these choices. This dissertation seeks to fill these gaps while arguing for a greater attention to the role of culture in furthering our understanding of all dimensions of private school choice. It does so – through distinct papers drawing on three sources of original data (interviews and a survey with parents engaging in school choice and interviews with private school administrators) – in three complementary ways: (1) by highlighting the centrality of culture and values in shaping parents’ preferences for and selection of private schools, (2) by showing how differences in access to field-specific cultural and social resources influence parents’ ability to fulfill their school choice wants and, by extension, to transmit advantages to their children, and (3) by drawing attention not only to the role of cultural matching in schools’ selection of students, but also to how these selection processes vary across schools with differing levels of selectivity and how parents’ cultural resources and dispositions interface with the interactive matching process.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".