How Leadership Moments are Enacted within a Strict Hierarchy: The case of kitchen brigades in haute cuisine restaurants
Bibliographic record
Abstract
This paper employs a strong process approach to leadership – one that focuses on leadership moments in action – to explore how collaborative leadership emerges within a hierarchical context. Drawing on observation in three haute cuisine restaurant kitchen brigades – highly hierarchical teams that deal with intense time pressures – we document empirically in the ongoing flow of experience how leadership moments reorient collective action as a response to an unstable environment. Moreover, we show how collaborative leadership emerges from a hierarchical structure, counterintuitively, during the most critical period of the service. Our contribution is twofold. We offer a novel conceptualization of the emergence of plural leadership within a hierarchical context, one that highlights the capacity to reframe the way of working together during the most critical moments of an unfolding situation. In addition, our work contributes to the strong process approach to leadership through the methodology adopted: rather than exploring how turning points are discursively enacted, we focus on these as manifested in action and in the non-verbal aspects displayed at such moments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".