Accessing Privilege: Teachers’ Experiences in Elite Private Schools
Bibliographic record
Abstract
Schools serve as a site of investigation for social reproduction, often with a lens on marginalized communities to elucidate how inequity and class disparities are actualized. Comparatively, there has been less access to private schools for researchers, and therefore, fewer studies that focus on the role of upper-class communities in maintaining these structures. This study explores the elite private school environment to come to an awareness of how privileged self-understandings are created in these institutions via educators’ perspectives. Privileged dispositions can be a barrier to building an equitable world as they are oriented toward self-fulfillment, often with little regard for one’s impact on others. Focusing on the experiences of teachers broadens existing research and creates space to think about the implicit and explicit ways educators relate to privilege for the purpose of critical reflection and change. \n\nTwo questions frame this endeavour: How do teachers working in elite private schools perceive and negotiate privilege in various spaces? Secondly, how might teachers’ experiences engender privilege or how might they challenge it in their everyday practices? This study explored these questions through a series of three in-depth interviews with eight middle and secondary educators in southern Ontario with varying degrees of experience in elite private schools. Using an iterative, thematic, and intersectional approach to data analysis, this study arrives at patterns of how privileged self-conceptions are formed, reinforced, and areas in which there are attempts to challenge them. Ultimately, this study finds that despite teachers’ attempts to confront privilege, they take part in reinforcing privileged self-understandings of their students. Educators feel they can teach about the topic but are limited in the extent they can challenge the privilege that pertains to students or parents. As well, teachers adopt their own privileged self-understanding and perpetuate exclusion based on race, gender, sexual orientation, and linguistic differences. These factors shape how they conceive of their responsibility and work as teachers in relation to students and colleagues. In making these distinctions, it becomes clearer what hidden discourses shape teachers’ experiences, who is most implicated by these narratives, and the power dynamics that exist in elite spaces.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".