Impact of the Black Lives Matter Movement on Culturally Responsive Teaching Practices of Teachers
Bibliographic record
Abstract
This research examines how the Black Lives Matter (BLM) movement impacts culturallyresponsive teaching practices of educators. Social movements refer to organized and continuous collective actions taken by individuals or groups who are not part of routine decision-making to bring about changes in institutions (Amenta & Polletta, 2019; Snow et al., 2004). One example of such a movement is BLM, a social movement that gained prominence following George Floyd’s murder. To gain a deeper understanding of BLM and its impact on educators’ instructional practices, a qualitative narrative research study utilizing semi-structured interviews was conducted with six elementary school teachers in the Greater Toronto Area (GTA) who valued and employed culturally responsive teaching practices. Centering the BLM movement, a thematic qualitative narrative analysis considered prior knowledge, environments, teaching strategies, intentionality, challenges, professional development, representation, and resource provision as key determinants in the development and sustainability of culturally responsive teaching practices. Overall, the findings in this study could help policymakers, researchers, and other stakeholders better understand the experiences of culturally responsive teachers and always support them optimally in their practice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".