The teacher-coach relationship- a relationship geared toward deep learning or increased institutional control?
Bibliographic record
Abstract
This paper examines relationship(s) between school coaches and teachers. The paper positions teacher ongoing professional learning and change through this relationship as a contested experience that exposes both qualities of rich learning and new practice as well as power relations and relative disempowerment. The paper is drawn from a larger interpretive case study that followed the experiences of district-based consultants working in a large-scale consultancy-based reform, the Secondary National Strategy (SNS), in London, UK from 2002-2006. The paper uses unstructured interviews to reveal the various and diverse experiences that occurred in schools as a result of the introduction of the consultant-teacher relationship into schools. From these experiences, a complex picture of the coach-teacher relationship appeared. Amongst other things, the SNS consultants provided opportunities for teachers to collaborate and share their work in ways that helped break some of the structural isolation within secondary schools, while also serving to further control teachers’ workspace and classroom activities. The potential for teachers to take on new curriculum, refine their practice and potentially grow new forms of pedagogy within their work will also presumably require district, federation, faculty and government supporting conditions that are responsive to school and teacher workspaces. Viewing an instance of intensive interaction between teachers and coaches provides insights into the challenge of new, refined or adapted forms of pedagogy becoming institutionalized within large, diverse public school systems.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".