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Record W7132710755

Pregled mjernih instrumenata namijenjenih mjerenju emocionalne inteligencije

2017· dissertation· hr· W7132710755 on OpenAlexaboutno aff
Ivana Ćosić

Bibliographic record

VenueRepository of the Faculty of Humanities and Social Sciences Osijek · 2017
Typedissertation
Languagehr
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaScale (ratio)Toronto Alexithymia ScaleRoot (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Temeljni teorijski modeli emocionalne inteligencije (EI) razlikuju se po stajalištu o njezinoj etiološkoj prirodi. U prvom, Mayer Salovey modelu, EI je određena kao izvorna sposobnost koja se odnosi na interakcijske procese između emocija i kognicije. U kasnije nastalim teorijskim modelima (Goleman, Bar-On) EI se određuje kao manifestacija kompozita ličnosti povezanih s emocijama. Neke od najpoznatijih ljestvica za samoprocjenu emocionalne inteligencije, operacionalizirane su prema modelu Mayera i Saloveya. Stupanj razvijenosti neke sposobnosti ili više dimenzija, određivao se na osnovu samoprocjena sudionika ili zapažanja ljudi u njihovom okruženju, a ne na osnovu zadataka učinka. Najpoznatija iz skupine instrumenata prema Mayer Salovey modelu je Schutte-ova skala (Self report emotional intelligence test, SREIT, Schutte, Mlouff, Hall, Haggerty, Cooper, Golden i Dornheim, 1998). Tett, Fox i Wang (2005; prema Hajncl i Vučenović, 2013) su konstruirali Višedimenzionalnu skalu EI (Multidimensional Emotional Intelligence Scale - MEIA). Po revidiranom modelu Mayera i Saloveya konstruirana je Wong-Law EI skala (Wong-Law EI Scale - WLEIS - Wong, Wong i Law, 2007). U Hrvatskoj su konstruirani; Upitnik emocionalne kompetentnosti (UEK-45; Takšić, 2000.b) i Upitnik regulacije i kontrole (negativnih) emocija (ERIK; Takšić, 2004.). Za procjenu emocionalno- socijalnih kompetencija koriste se Bar-Onov Inventar emocionalnog kvocijenta (Emotional Quotient Inventory, EQ-i; Bar-On, 1997) i Torontska skala aleksitimije (Toronto Alexithymia Scale - TAS - 20; Bagby, Parker i Taylor, 1994). U novije se vrijeme naglašava važnost pristupa ispitivanju EI kao sposobnosti, odnosno testovima koji bi zahtijevali rješavanje neke problemske situacije i pronalaženje točnog odgovora. Bez obzira smatramo li je dijelom mentalnih sposobnosti ili skupom preferiranih ponašanja, za prikladno socijalno ponašanje, upravljanje emocijama, smatra se nužnim dijelom zajedničkog konstrukta. Poželjno je da se EI jasnije odredi, ali u psihologijskim istraživanjima upitni konstrukt vodi napretku i dostignuću (Kulenović i sur. 2006; prema Hajncl i Vučenović, 2013).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0050.005
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
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 head0.115
GPT teacher head0.375
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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