Giving meaning to women teachers' life histories and political commitments in the classroom
Bibliographic record
Abstract
The focus of the present research on teachers' life histories and teaching practices or the personal and professional connections in teaching, sets out to study how ‘teachers as intellectuals’ or ‘transformative educators’ are working toward emancipatory/transformative practices in the classroom. Transformative pedagogy aims to redress the experience of marginality which students of colour experience in mainstream school curriculum which overlooks or undermines the histories of people of colour. Transformative teaching approaches include critical pedagogy, critical multicultural education, critical literacy, culturally response pedagogy and democratic processes in teaching and learning. Transformative educators place the study of marginalized communities at the center of the curriculum in order to counteract student marginality and disengagement. While traditional studies of teachers' lives and the genre of making explicit personal and professional links have reproduced the dominant North American conceptualization of ‘a good teacher’ as connoting someone who is ‘usually white’ and ‘middle class,’ current focus on teachers' lives and ethnography in today's contemporary classrooms, takes on a different approach by reflecting the changing ethnicity of teachers. Similarly the present research ethnography takes an interest in understanding how on one hand, teachers' awareness of their social locations (or their race, class and gender) and their political ideological commitments with the other, influence curriculum choices and teaching practice that contribute toward counteracting inequalities in the school system. The narrative texts of two teachers' life histories and teaching practices are analyzed using feminist interpretive theories that represent conversations across social differences and racial identities which includes South Asian Indo-Canadian and Euro-Canadian ethnicities. The classroom ethnography with the two teachers, who are identified as ‘transformative educators’ working toward social change, took place in a multi-racial, low socioeconomic elementary school community in the Greater Toronto Area. The community itself underwent fundamental demographic changes over the past twenty years, from a largely blue collar working class Portuguese/Italian community to the present largely South Asian (Indian, Sri Lankan) and Caribbean blue collar working class and underclass community. The research was carried out between March 1999–August 2000.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".