Science Teacher Education in Canada: Addressing Diversity by Living and Teaching Intersectionality
Bibliographic record
Abstract
Abstract As a Black woman of Caribbean heritage, born and raised in England, my own school science experience was focused on learning the tricks that teachers presented as intuitively graspable. I was used to pushing through and ignoring the ‘outsider’ feelings that I possessed. As a science teacher and science teacher educator, I came to understand that there are many students for whom the acquisition of science knowledge means compromise to their sense of selfhood, either because they are members of groups for whom Western modern science is not a central tenet of understanding or because of the esoteric mode of science instruction. In this chapter, I identify four critical incidents that have occurred during my professional experience as a science teacher educator. I explore the implications of these incidents by examining them through the equity lens of intersectionality to highlight broader concerns in science teaching and science teacher education. The analyses reinforce the need for science teachers to allow themselves and their students to be open and reflective about their own positionings in the field of science education as well as the need to acknowledge the historical and philosophical contexts of Western modern science as a body of knowledge. I hope that this chapter will be used by science teacher educators to stimulate dialogue and provide an artifact around which constructive and meaningful conversation foments in the many spaces of science teacher education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.080 | 0.027 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".