PRIVATE SCHOOLS FOR PUBLIC GOOD: A collective case study of social justice-oriented teachers in an elite independent school
Bibliographic record
Abstract
In this collective case study, I explored teachers’ understandings and enactments of social justice education, and the ways in which both are shaped by an “elite” school institution, as defined by Gaztambide-Fernández (2009). A maximum variation sampling strategy was used to recruit five teacher-participants–each their own “case”—in one elite independent school in Ontario, Canada. Data collection methods included two narrative interviews, 8-10 classroom observations per participant, and document analysis; approaches to data analysis included interactional narrative analysis, thematic analysis, and critical discourse analysis, respectively. I was guided by the following questions: (1) How do teachers in Ontario’s elite independent schools, who express a commitment to social justice education, understand it? (2) How are these teachers’ understandings of social justice education enacted? (3) How do these teachers navigate the school’s traditions of institutional oppression in their understandings and enactments of social justice education? Despite participants’ evoking critical theories of social justice (namely, allyship, white accountability, anticolonialism), their enactments were contradicted and contained by their agency for—and attachment to—the school. Participants were driven by an imagined “better” future for the school, and for society, led by “transformed” elites. Yet, insofar as participants’ imaginings go, the school will not change in the one way that would matter the most: acting in opposition to their class interests. In this study, I use the analogy of Alice’s “wonderland” to contextualize participants in a fantastical and re-storied “educational wonderland” wherein their understandings and enactments are driven by futures dreamt up for their students, futures that settle their internal conflicts, too.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.059 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".