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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".