Challenges and Possibilities of Scaffolding Critical Reflection and Cultural Responsiveness for Pre-Service Special Educators
Bibliographic record
Abstract
Given the nature of their responsibilities in field-based settings, university supervisors play an important role in preparing pre-service teachers to become culturally responsive and critically reflective special educators. However, supervisors themselves may not have the experience and training necessary to do so, and limited guidance is available regarding effective mentorship practices to foster implementation of culturally and linguistically responsive pedagogy (CLRP) and critical reflection. This exploratory qualitative study examined how three supervisors engaged in post-observation conferences with their student teachers to promote critical reflection about CLRP using content and discourses analyses. Findings indicated that, although student teachers engaged in discussions about CLRP and were able to critically self-reflect, supervisors were unable to facilitate critical reflection vis-à-vis institutional practices and systemic bias. Theoretical and practical implications for supervision of practicum experiences in pre-service teacher education programs are offered.
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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 | 0.103 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".