Persuading Pre-Professionals to be Participant Observers: Reflections on Teaching Anthropology and Education to Professional Teacher Candidates
Bibliographic record
Abstract
This paper explains how I design and teach an Anthropology and Education course within a professional teacher education program. After establishing how some teacher candidates might initially imagine that this course is irrelevant to their professional education, I argue that anthropological knowledge and being able to think anthropologically enables teacher candidates to become better teachers. Specifically, I argue that becoming a participant observer of one’s own and others’ practices provides an easily accessible crossover between an anthropological method and mindset, on the one hand, and teacher actions like instruction, observation, assessment, and reflective practice (Schön 1982), on the other. To support this claim, I describe how I teach teacher candidates concepts and theory from anthropology that are applicable to the study of education, and can be used to inform their work on a video ethnography of a classroom (Hester 2012) that I assign to develop their practice of professional participant observation. I then describe how I prepare teacher candidates to consider the context of that video, especially as it offers an encounter with ideological diversity within the teaching profession and schools. The conclusion explains how I encourage the candidates to continue using the participant observer concept to inform their professional work post-graduation.
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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 | 0.047 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".