Coherence-Making in a Graduate Teacher Education Program: Policy Intended to Policy Lived
Bibliographic record
Abstract
This paper investigates coherence in a large, urban graduate teacher education program at a research intensive university which has undergone change in response to recent governmental policy designed to ‘enhance’ conceptual coherence in teacher education. Using qualitative data from multiple open-ended surveys and focus groups, we analyze and report on teacher candidates’ experiences of coherence in the program. Findings indicate that coherence is made by candidates in an ongoing, personalized process, that structure both supports and challenges coherence and that foregrounding coherence can inform program review and increase explicit efforts to develop opportunities for coherence-making by instructors and candidates.
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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 | 0.052 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".