Complexity Science and Cohorts in Teacher Education
Bibliographic record
Abstract
In this paper we examine the nature of our self-study practices in an elementary teacher education cohort called CITE (Community and Inquiry in Teacher Education). We argue that self-study is not only important to our continued work in CITE but also a critical feature of professional practice in general. Two general questions frame our analysis: (1) What is significant about cohorts in teacher education? (2) How might complexity science inform our understanding of cohorts in particular and of teacher education programs in general? We argue in the paper that the use of a cohort-type structure in a teacher education program provided us with flexibility and potential for improvisation to address the perennial problems of program fragmentation. To better understand our own teaching and learning practices in this community setting, we sought an analytic framework that emphasized the importance of the learning potential of the collective as opposed to just the learning potential of the individual. We argue that complexity science, with its ecological emphasis on learning systems, is such an analytical framework. We generate six propositions about the role and value of cohorts in teacher education that arise from self-study of our own practice.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".