“Don’t You Know That Only White Kids Like Science?”: Currere as Critical Autobiography
Bibliographic record
Abstract
Assessment results demonstrate a persistent achievement gap in science between Black, Latino, and Aboriginal students and their Caucasian peers. While curriculum documents espouse a Science for All slogan, little guidance is provided on the pedagogical actions that teachers can take to improve the teaching and learning of science for all students. This critical autobiography mobilizes the transformational potential of William Pinar’s method of “currere” as self-study. My review of the literature indicates scant evidence of currere’s conceptual and theoretical use by teacher educators who are preparing pre-service teachers. In this article, I employ currere as a means of self-reflection from my position as a teacher educator and Ph.D. student at the Ontario Institute for Studies in Education (OISE) of the University of Toronto. I document and analyze my efforts to navigate within and through the regressive, progressive, analytical, and synthetic moments as espoused by Pinar. Ultimately, currere enables me to unleash fresh ways of conceptualizing a preservice science methods course with a social justice focus.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".