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Record W1977022341 · doi:10.1108/02621710410537056

Emotional intelligence

2004· article· en· W1977022341 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

VenueJournal of Management Development · 2004
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterpersonal communicationLeverage (statistics)Emotional intelligenceFunction (biology)Knowledge managementTask (project management)Matrix (chemical analysis)PsychologyComputer scienceBusinessSocial psychologyArtificial intelligenceManagementEconomics

Abstract

fetched live from OpenAlex

Organizations continue to employ the matrix organizational form as it enables companies to use human resources flexibly, produce innovative solutions to complex problems in unstable environments, increase information flow through the use of lateral communication channels, and leverage economies of scale while remaining small and task oriented. Despite its strengths, the matrix has inherent problems. Earlier studies have primarily addressed structural problems. In this paper, we identify four interpersonal challenges that impede matrix performance: misaligned goals increase competition among employees, roles and responsibilities are unclear, decision‐making is untimely and of possibly low quality, and silo‐focused employees do not cooperate. We propose that emotionally intelligent employees can function better in the matrix. We offer solutions for both managers and employees to improve performance in matrix organizations by applying the four components of emotional intelligence, specifically, managing, understanding, using, and perceiving emotion, to each interpersonal challenge.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.047
GPT teacher head0.332
Teacher spread0.285 · 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