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Leadership and Followership from a Social Cognition Perspective: A Dual Process Account

2012· book-chapter· en· W2626485769 on OpenAlexaff
Douglas J. Brown

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicManagement Theory and Practice
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFollowershipDual (grammatical number)Perspective (graphical)CognitionProcess (computing)ConnectionismSchematicCognitive scienceInformation processingPsychologyFunction (biology)Information processing theoryComputer scienceKnowledge managementCognitive psychologySocial psychologyArtificial intelligenceEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Abstract This chapter explores the leadership literature through the lens of a dual processing perspective, indicating that human cognition consists of an automatic system (System 1) and a conscious system (System 2). In particular, it addresses follower and leader information processing from the dual-process paradigm. It is observed that quick, nonconscious processing plays a tremendous role in leadership and followership. The nature, acquisition, retention, and retrieval of information in System 1 follow a connectionist architecture, and function in accordance with the properties that have been typically attributed to schematic knowledge. The ability to engage System 2 has been compared to a muscle, which needs time to recover. In general, the data indicate that leadership scholars may want to consider mental events through the lens of an old, yet increasingly dominant, human-information-processing paradigm: the dual-process model.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.067
GPT teacher head0.235
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2012
Admission routes1
Has abstractyes

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