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Record W2047835700 · doi:10.1177/1534484312440566

Learning to Lead, Unscripted

2012· article· en· W2047835700 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHuman Resource Development Review · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsImprovisationHEROPsychologyLeadership developmentSociologyComputer sciencePublic relationsArtificial intelligencePolitical scienceArtVisual arts

Abstract

fetched live from OpenAlex

We argue that improvisational theatre training creates a compelling experience of co-creation through interaction and, as such, can be used to build a distinctive kind of leadership skills. Theories of leadership as relational, collaborative or shared are in pointed contrast to traditional notions of an individual “hero leader” who possesses the required answers, and whom others follow. Corresponding thinking on how to develop these newer forms has, to date, been relatively rare. In this article, we draw on recent research to identify three core principles for learning affiliative leadership. We then apply literature on improvisational theatre and its main skill areas to build a model of developing affiliative leadership, and illustrate the model through an improvisation workshop in which participants learn the skills and principles that it sets out. The model and workshop may serve as useful tools for those searching for methods to develop leadership in contemporary organizations.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.035
GPT teacher head0.251
Teacher spread0.215 · 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