A Communities of Practice Approach to Field Experiences in Teacher Education
Bibliographic record
Abstract
This article argues that prospective teachers who have the most productive experiences withinpre-student teaching field experiences are those whose field sites allow them to become membersof communities of practice, the conditions of which, according to Wenger (1998) include jointenterprise, mutual engagement, and shared repertoire. Employing interviews and contentanalysis of documents, the researchers explored the experiences of a cohort of teacher candidatesin a pre-student teaching practicum to better understand elements of field experience that mightinfluence identity development. We highlight the cases of two prospective teachers as illustrativeand contrasting experiences of the cohort as a whole. We conclude by offering recommendationsfor how teacher education programs might assist prospective teachers with negotiating forconditions within field sites that allow for productive participation and growth.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".