Comparing “incomparable” schools? Extended case method and reflexive comparative study of teaching practices in Brussels and São Paulo
Bibliographic record
Abstract
Comparing dissimilar social realities inexorably raises the question of the way to establish a basis for comparison that respects the specificities of the cases studied while not at the same time considering the context as negligible. Seeking to highlight the contributions of Burawoy’s extended case method to addressing these challenges of comparative research, this article retraces the iterative design process of a multicase socio-ethnographic study of teaching practices carried out in Brussels and São Paulo public high schools. Revealing a sharp contrast between a teaching to transgress kind of critical pedagogy in the Brazilian case and a more conventional form of status quo education for democratic citizenship in the Belgian one, the research thus sheds light on the role of contextual dynamics in the emergence of these ideological divergences between two instances of public education. Unlike the inductive approach and its logic of generalization based on the search for lowest common denominators—relegating context and case specificities to the background—this article thus argues that the least intuitive comparisons are, in fact, the most likely to stimulate sociological imagination, questioning the obvious in the studied societies and opening up to a deeper understanding of each case.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".