The Concept of Spontaneity and its Relationship with the Individual Characteristics of Personality
Bibliographic record
Abstract
In this article, the authors consider the concept of spontaneity and the relationship of this characteristic with such individual personality characteristics as locus of control, autonomy, tolerance to uncertainty, creativity, self-regulation, flexibility in communication and spontaneity itself. The authors conduct an experiment that allows to trace this relationship. The research involved 205 people between the ages of 18 and 40, including 125 people living in Ukraine and 80 people living and studying abroad. The Ukrainian study group consisted of 82 women and 43 men. The group of foreign researchers consisted of 52 women and 28 men. To study the impact on the spontaneity of the sociocultural experience of the individual, representatives of other communities aged 18–39 were involved. The sample of foreign respondents was divided into 4 groups: countries in Europe, countries in Asia and Africa, CIS countries and other countries in the world 80 people in total. In the study, the authors conclude that spontaneity does not exist separately from other personality characteristics, is not an independent personal property that develops according to its own laws, but is closely connected with many important personality formations and structures, and in order to understand how these connections function, it is not enough to investigate spontaneity separately from other personality traits.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".