Neutrality Always Benefits the Oppressor: The Need to Rupture the Normalized Structure of Teacher Education Programs to Diversify the Workforce
Bibliographic record
Abstract
As faculties of education have undergone drastic changes to keep teacher education programs afloat while accommodating teacher candidates during a pandemic, much of these altercations are designed, much like the education system itself, to meet the needs of white, privileged students. Although many of the changes from classroom content, pedagogy, and assessment to alternative practicums are commendable in the face of a pandemic, BIPOC and teacher candidates from lower socioeconomic status, who are already underrepresented in the Ontario teacher workforce, are further disadvantaged due to existing inequities and opportunity gaps (Battiste, 2013; Colour of Poverty, 2019; Henry & Tator, 2012) exasperated by pandemic conditions. In this chapter we ground our experiences through a duo-ethnography as two racialized faculty members within teacher education programs at Canadian postsecondary institutions. It is argued that the implications of the pandemic in convergence with the axiology of whiteness and white privilege that define teacher education and the teaching profession in Ontario operate as a double barrier to entry into and diversification of the teacher workforce. Suggestions are made for how to disrupt and rupture the normalized structure of teacher education programs and its policies and practices to advance equitable outcomes.
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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 | 0.010 | 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 | 0.028 | 0.056 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".