Confronting neoliberal education policies and COVID-19: Convergent trajectories of public school teacher resistance in Brazil and the United States
Bibliographic record
Abstract
This paper uses comparative ethnography to examine the global spread of neoliberal education policies and public school teacher resistance to them in Brazil and the United States. Initially focused on teacher responses to São Paulo’s Programa Educação Compromisso (Commitment to Education Program), the study was expanded into a cross-national comparison with US education reforms that Brazilian elites promoted as successful and imitable models. The aim of the study thus became to compare local forms of teacher resistance to elite-driven global policy models. However, when the COVID-19 pandemic exploded between rounds of data collection, it created both new challenges and opportunities for the comparative ethnographic study. The pandemic introduced practical constraints that prevented a traditional ethnographic comparison, but it also made more visible the crucial role of public schools in guaranteeing the basic rights of school communities, and revealed how teachers’ collective responses to a global health crisis were informed by prior educational policy reforms. Ultimately, the paper argues that both elite-driven policy mobility and the global pandemic contributed to convergent trajectories in distant and historically distinct public school systems, imposing new challenges on teachers but also galvanizing similar practices of resistance and community engagement.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".