Supporting Urban-Oriented Teacher Candidates to Experience Rural Schooling: The Story of a Virtual Adapted Practicum
Bibliographic record
Abstract
In the fall of 2020, due to the institutional impacts of COVID-19, the Master of Teaching Program in the Ontario Institute for Studies in Education, University of Toronto (Canada) transitioned to a modified practicum program. In this article, I draw on self-study (Kitchen et al., 2020) to examine and share my experiences as a Practicum Advisor tasked to design and deliver a four-week virtual practicum program for 30 teacher candidates, without access to high school classrooms. I reflect on how my rural teacher and researcher selves informed my practicum design in one of Canada’s largest urban faculties of education, including teacher candidates’ development of data portraits based on one rural case study high school. A virtual adapted practicum presented me with a narrow opening, in an otherwise urban-dominant curriculum, to expand teacher candidates’ gaze beyond the metropolis.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".