Coach development as assemblage: mobilising assemblage thinking to examine coach learning within an endurance-running coach development intravention
Bibliographic record
Abstract
In this paper, we examine the learning of nine high-performance endurance running coaches over a seven-week poststructuralist-informed coach development workshop. Drawing on Deleuze and Guattari’s concept of assemblage as a novel analytical framework we explore the production of difference within the context of our learning assemblage, and why thinking and coaching differently remain challenging. Connecting content (e.g. coach learners, coach developers, learning materials and technologies, virtual and physical spaces) and expression (e.g. coaching norms and statements, privileged coach development knowledges and curricula) within a range of empirical materials generated throughout the workshops made visible multiple sociomaterial forces that reproduce coaching as a modernist formation, but also, more hopefully, possible lines of flight for coaches and coach developers (new ways of thinking and practicing) that have the potential to reconfigure endurance-running coaching in ways that are arguably more ethical and sustainable. We conclude by discussing the implications of these for the planning and doing of poststructuralist informed coach development work.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".