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Record W2321735563 · doi:10.1037/gdn0000039

Measuring team emotional intelligence: A multimethod comparison.

2015· article· en· W2321735563 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGroup Dynamics Theory Research and Practice · 2015
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsReferentPsychologyMeasure (data warehouse)Emotional intelligenceIncremental validityConstruct validitySocial psychologyDevelopmental psychologyPsychometricsComputer scienceData mining

Abstract

fetched live from OpenAlex

This study compared 3 measures of the team emotional intelligence (EI) construct: an individual-referent subjective measure, an individual-referent performance measure and a team-referent measure. Results showed that when using emotion-related variables (e.g., team relationship conflict and cohesion) as criterion variables, the team-referent EI measure was the strongest predictor and demonstrated incremental validity over both of the individual-referent measures. Furthermore, the individual-referent subjective measure demonstrated marginal incremental validity over the individual-referent performance measure. These results were not found when task-related variables, such as task conflict and performance, were used as criteria. Implications of the results are discussed.

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)0.019
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.915
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.418
GPT teacher head0.518
Teacher spread0.100 · 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