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PSYCHOLINGUISTIC FEATURES OF IMAGINATION AS A COMPONENT OF LUDIC COMPETENCE

2020· article· en· W3014567251 on OpenAlexfundno aff
Юлія Кобзєва, Iia Gordiienko-Mytrofanova, Serhii Sauta

Bibliographic record

VenueEUREKA Social and Humanities · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
FundersUniversität ZürichUniversity of Ottawa
KeywordsCompetence (human resources)ConsciousnessPsychologyPsycholinguisticsStimulus (psychology)Cognitive psychologyLinguisticsCognitive scienceCognitionSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Ludic competence is an integral part of the professional competence of would-be psychologists; the psycholinguistic features of imagination are in turn an integral component of the ludic competence. We used the method of applied psycholinguistic research in order to define and explain the psycholinguistic features of imagination as a component of the ludic competence. The main stage of the research was a free association test with the stimulus word “imagination”, as the most elaborated technique of semantic analysis. The psycholinguistic features of imagination as a notion that belongs to the inner world and as a component of the ludic competence were reflected in everyday linguistic consciousness as three core (more than 10 %) semantic clusters: (a) associates that reflect psychological processes and states (54.5 %); (b) associates that are connected with creative activity (25.5 %); and (c) associates that describe the outside world (11 %). Imagination was mostly represented by lexemes with abstract semantics. The semantic content of the word “imagination” did not depend on gender identification. Both male and female respondents showed a positive emotional attitude to the stimulus “imagination” and evaluated it as something positive. Our data confirm that the psycholinguistic experiment and the method of free association, in particular, can be extensively applied beyond linguistics and prove to be rather effective.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.082
GPT teacher head0.349
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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