Teacher Professional Development in Brazil: Colonization of Teachers’ Voices
Bibliographic record
Abstract
This paper focuses on professional development practices implemented in Brazil, and the influence of post-colonial views in the power-relation between the ‘educational authorities’ or ‘experts’ and teachers. The paper addresses how this relationship in professional development is mostly ‘one-sided’, as often it does not include the 'voices' of teachers. Rather, it prioritizes the assumptions many ‘experts’ have towards teachers’ needs for growth, in which the choices of topics and the kind of professional development programs to be designed often follow an ‘one-fits-all’ model or banking education as defined by Freire (1970). This paper emerged from the author's experience during her Master’s thesis research (Nascimento, 2010). She addressed the challenges of teaching in public schools in Brazil, and its implications in a social justice context. Through the lens of different teachers who participated in a volunteer-based Canadian/Brazilian teacher professional development program, the research investigated in which ways the inclusion of teachers' voices in professional development programs could affect teachers’ performance in a Brazilian context. During four years over the summer, Canadian teachers and Brazilian teachers worked together on a professional development program that aimed to encourage teachers to share their teaching experiences and reflect on their practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".