Reframing mentoring in initial teacher education: a Bourdieusian analysis of pre-service teachers’ experiences amidst policy reform in England
Bibliographic record
Abstract
Purpose This study investigates mentoring practices within Initial Teacher Education in England during a period of significant policy reform. Informed by Bourdieu’s theory of capital and habitus, the research explores how pre-service teachers (PSTs) experience mentoring relationships and how these experiences shape their professional identity and development. Design/methodology/approach Through focus group interviews, the study examines the distribution of economic, cultural, social and symbolic capital within school placements and the power dynamics inherent in mentor-mentee relationships. Findings reveal considerable variability in the quality and inclusivity of mentoring, with unequal access to support, feedback and professional opportunities impacting PSTs’ confidence, sense of belonging and agency. Findings The study highlights how one-size-fits-all mentoring frameworks risk reproducing inequalities, particularly for PSTs from marginalised backgrounds. It calls for mentor training that moves beyond procedural compliance to incorporate equity-focused, individualised support strategies. Originality/value The paper concludes by advocating for mentoring models that explicitly address social justice, professional inclusivity and the complex relational dynamics that shape teacher development in a highly regulated education system.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".