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Record W2117000775 · doi:10.1037/a0038201

Divide and conquer: When and why leaders undermine the cohesive fabric of their group.

2014· article· en· W2117000775 on OpenAlexaff
Charleen R. Case, Jon K. Maner

Bibliographic record

VenueJournal of Personality and Social Psychology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsGroup cohesivenessSocial psychologyPrestigePsychologyCohesion (chemistry)Dominance (genetics)Power (physics)

Abstract

fetched live from OpenAlex

Cohesion, cooperation, and the formation of positive bonds among group members are key processes that facilitate effective group functioning. Consequently, group leaders usually work to enhance the positive social bonds among group members to facilitate cooperation and group cohesion. The present research suggests, however, that leaders sometimes are motivated to generate divisions--not cooperation--among their subordinates. Although such divisions may undermine group functioning, they can also serve as a means of protecting the leader's own power. Four experiments supported the hypothesis that, when they perceive their power to be threatened, leaders create divisions among their subordinates in order to protect their power and reduce threats posed by potential alliances among those subordinates. Leaders restricted the amount of communication among subordinates (Experiment 1), physically sequestered subordinates (Experiment 2), and prevented subordinates from bonding with one another interpersonally (Experiments 3 and 4). Those behaviors were observed only among dominance-motivated leaders (not prestige-motivated leaders), and were directed only toward highly skilled (and thus highly threatening) subordinates. Consistent with the hypothesis that leaders' behavior was driven by a desire to protect their power, the tendency to prevent in-group bonding was eliminated when leaders were assured that their power could not be lost (Experiment 4). These results shed light on factors that may undermine positive social processes within groups.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.364
Teacher spread0.287 · 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.

Study designQualitative
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

Citations141
Published2014
Admission routes1
Has abstractyes

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