Psychological Features of Formation of Emotional-Will Sphere of Athlete Personality
Bibliographic record
Abstract
At present the issue of "personal profile" of the athlete engaged in a certain sport is quite widely discussed.However, the analysis of personal features of athletes of the international class on sports did not confirm the hypothesis of "personal profile."More productive was the approach of finding common manifestations of personality that lead to success in sports.Among such features should be: sensivity, emotional resistance, activity in overcoming obstacles.According to American sports psychologists, the most common personal traits of athletes include:-High level of aggressiveness (which is almost always under control of the athlete of high class), -High level of achievement motivation, -Extroversion and character hardness, -authoritativeness, -Emotional stability and self-control [1].Aggression.In many sports, especially those where direct physical contact is allowed, various forms of controlled physical aggression are simply necessary.Research data show that high-class athletes engaged in these sports are not only more aggressive, but also tend to express their aggressive tendencies more freely than representatives of the so-called normal sample.At the same time aggressiveness is expressed in different ways in athletes engaged in different sports.Motivation to achieve success in athletes.Motivation is a key variable of mastering sports skills, the ability to demonstrate them.Motivation is, first of all, a desire for success, high results in its activities.And if a person, seeks to achieve success, high results in activity,
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".