“Things your history teacher won't teach you: Science edition”: Black women science teachers as anti‐racist teachers
Bibliographic record
Abstract
Abstract Marginalized communities cannot and do not have decontextualized experiences with how socioscientific issues, such as exposure to COVID‐19 as frontline essential workers, high Black infant mortality rates, air pollution leading to respiratory problems, and other issues, affect their communities. As PreK‐12 science teachers and teacher educators strive to dismantle oppressive practices in their classrooms and curriculum, it would be helpful to learn from Black women science teachers who have been engaging in anti‐racist practices before the racial awakenings of Summer 2020. In this study, three different virtual focus groups, or Sista Circles, were conducted with 18 Black women secondary science teachers. Ranging from 1 to 22 years of experience, Black women teachers across the country and international participants in Canada and Qatar participated in the Sista Circles. From intersectional qualitative analysis and narrative inquiry, the findings of the study reveal that Black women science teachers enact anti‐racist science teaching by bringing something new to the community; using NGSS standards within the context of the community; teaching at the intersection of history, culture, and science learning and teaching; and building critical consciousness in the science classroom. Furthermore, the findings of the study have implications for the use of anti‐racist frameworks within the context of science education that were authentically the practices of the Black women in the study. This study offers insights into how the critical consciousness of Black women teachers can be represented in the science classroom even in times of nonsupport from peers and administration. The power and necessity of Black women teachers are paramount in science classrooms specifically because of the neutral, apolitical ways science teaching has been approached in the past. The narratives and stories shared here exemplify how Black women science teachers transform science teaching and learning by displaying various acts of Criticality.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".