Reimagining teacher educators: emotional geographies of mentor teacher identity development in urban teacher residency partnerships
Bibliographic record
Abstract
Purpose This qualitative case study aims to examine the professional identity development of mentor teachers as school-based teacher educators in an urban teacher residency program. Specifically, the study explores how mentor teachers navigate their roles and relationships with pre-service teachers, focusing on the emotional geographies that shape their practices and identity formation as teacher educators. Design/methodology/approach Utilizing a qualitative case study design, this study focuses on three experienced mentor teachers within an urban teacher residency program. Data were collected through paired interviews, focus groups, vignettes, classroom observations and semi-structured individual interviews. Content analysis was used to explore how mentor teachers’ interactions and relationships with pre-service teachers influenced their professional identity development as school-based teacher educators. Findings The findings reveal that mentor teachers' professional identities evolve through complex relationships with pre-service teachers. The study identified three key themes: (1) building relationships grounded in shared social justice goals, (2) reenvisioning teaching and learning practices through collaborative proximity with residents and (3) navigating power dynamics in the classroom to share responsibilities and develop a deeper understanding of their dual roles as teachers and teacher educators. Research limitations/implications This study expands the discourse on teacher education by emphasizing the under-recognized role of school-based mentor teachers in bridging the gap between theory and practice and offers insights into the relational and emotional aspects of teacher identity formation. Originality/value This research contributes to the growing field of teacher education by highlighting the importance of mentor teachers in advancing teacher preparation within collaborative urban residency programs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.012 |
| 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".