Teacher Educators’ Apprenticeships of Observation and Community-Based Field Settings
Bibliographic record
Abstract
This paper presents findings from a 2-year collaborative self-study examining four teacher educators’ (TEs’) experiences facilitating community-based field experiences in the United States and Canada. To examine our experiences working in these field settings we drew experiential learning theory (ELT) as well as the concept of apprenticeship of observation. Facilitating preservice teachers’ (PSTs) learning in field settings outside traditional PK-12 contexts, such as museums and a construction site, prompted us to consider how apprenticeships of observation and ELT intersect when seeking to expand PST education to also include community-based field settings. Working in these community-based field settings also served to disrupt some of our own apprenticeships of observation. Finally, we noted that when working in these non-traditional field settings and utilizing the ELT framework, our experiences as TEs were neither sequential nor unidirectional.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".