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Record W4304191939 · doi:10.1177/01708406221134225

How Leadership Moments are Enacted within a Strict Hierarchy: The case of kitchen brigades in haute cuisine restaurants

2022· article· en· W4304191939 on OpenAlexaff
Josée Lortie, Laure Cabantous, Cyrille Sardais

Bibliographic record

VenueOrganization Studies · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsConceptualizationPluralCognitive reframingContext (archaeology)Shared leadershipSociologyTransactional leadershipAction (physics)ForegroundingHierarchyCollaborative leadershipLeadership styleLeadershipPublic relationsPolitical scienceSocial psychologyPsychologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.248
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2022
Admission routes1
Has abstractyes

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