Dominant or Underrepresented: How Social Position and Program Context Impact Teacher Candidates’ Professional Identity Formation
Bibliographic record
Abstract
While Ontario classrooms have increasingly diverse student populations, underrepresented groups remain poorly represented in the teaching workforce and in many teacher education programs (Holden & Kitchen, 2018). The purpose of this qualitative study was to examine how teacher candidates (TCs) from underrepresented and dominant groups negotiate the process of professional identity formation in the sociocultural context of their teacher education program. The data for this study was collected using three semi-structured interviews over the course of the consecutive education program at Ontario University (OU). The participants were purposely sampled TCs from the dominant group (N=6) and underrepresented group (N=5) (Fraenkel & Wallen, 2003). My study was informed by phenomenological approach to qualitative inquiry and used multiple qualitative methods embedded in the social-constructivist paradigm (Guba & Lincoln, 1989. The phenomenological approach allowed me to study and understand the underlying structures that influence the professional identity formation for TCs by focussing on their experiences in the context of their program (Merriam & Tisdell, 2016). The findings from this study brought forth the distinct uniqueness that TCs from each group bring with them while starting the program; guided by their social position and lived history. The teacher education program and its context (coursework, educators, practicum placements etc.) over the course of the program implicitly or explicitly impacted the process of professional identity formation for the TCs. While the TCs from dominant group perceived their engagement in the program as only partially beneficial due to their existing knowledge about the profession and rather a means through which they were able to re-affirm their perceptions of self as teachers; TCs from underrepresented groups experienced the program as a cautionary tale of potential experiences of bias and discrimination due to their ethnicity, accent, disability, sexuality, immigration history etc. Overall, the findings from the study bring forth the role of teacher education programs beyond the sites for preparing the next generation of teachers but also a site for the negotiation of identity formation that accompanies the process of learning in the teaching community that supports and perpetuates the dominant structures and ideology.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".