PSYCHOLINGUISTIC FEATURES OF IMAGINATION AS A COMPONENT OF LUDIC COMPETENCE
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".