Learning Together about Culturally Relevant Science Teacher Education: Indigenizing a Science Methods Course
Bibliographic record
Abstract
This paper captures our co-learning, two science teacher educators, about indigenizing a science methods course in Canada. A self-study was conducted in the context of a pilot bachelor of education program (IBED) for a group of Indigenous students, to engage ourselves in reflective conversations about transforming the curriculum of a science methods course and making it culturally relevant for pre-service science teachers. The purpose was to determine our tacit and personal knowledge, as it contributes to our understanding of inclusive science education practices. In particular, we focused our conversations on written reflection about the perceived effectiveness of pedagogies used by Saiqa, the first author, who was the course instructor. Karen, the second author and critical friend, carefully examined these reflective narratives and provided comments, which were then considered in the context of other course materials, to initiate an ongoing dialogue about culturally relevant teaching (CRT) as it relates to science teacher education. The findings are framed using an art- based concept, a circle, to present our co-learning journey. This allowed us to connect our personal histories to our role as inclusive science teacher educators in the present, and to consider our future aspirations to indigenize our science methods courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.044 | 0.029 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.012 |
| 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".