Letting the Light In: A Collaborative Self-Study of Practicum Mentoring
Bibliographic record
Abstract
This article documents a year-long collaborative self-study of three teacher educators engaged in a practicum innovation in a teacher education program. This study, which is part of a larger study examining practicum-based seminars called Particulars of Practice (POP), focuses on exploring our own practices and identities within this innovation. Given this new structure in the program, we had many questions about how we each engaged with mentoring within this innovation, and what conceptions and assumptions were being surfaced for us about our roles, identities, and practices in practicum mentoring. The data included an email thread, personal reflections, and collaborative meeting transcripts that represented our experiences with the POP innovation. Using braiding as a methodological approach to honour all three sets of data, we were able to generate results that fell into two categories: reflections on the nature of self study and the knowledge gained about our identities, practices and roles from this research. We assert that the nature of self study involves vulnerability, difficult conversations, and multiplicity of perspectives. The knowledge gained from our collaborative self-study is identified as challenging preconceptions, seeing teacher candidates in new ways, and learning as a mirroring process.
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".