Level Of Aggression Among The Athletes Of High And Low Self-Efficacy
Bibliographic record
Abstract
Research Background: According to Scheier and Carver (1992) self-efficacy underlines people‘s faith in their ability to carry out particular behaviour or produce a desired outcome. Self-efficacy is a person‘s belief in his or her ability to complete a future task or solve a future problem. Other hand, aggression is distinct from, synonymously used words like anger and hostility. According to Fraczek and Zumkley (1992), Aggressive behaviour is aimed at causing harm to others. In this study investigator find out the impact of self-efficacy on level of aggression. Objectives: The study objective was, to study the significant difference in level of aggression among the athletes of high and low self-efficacy. Other hind side objective was, to study the significant difference in level of aggression among male and female athletes. Procedure: The present study, one hundred twenty athletes were selected. Out of them, sixty athletes were selected from high self-efficacy, in which thirty were selected from male athletes and thirty were selected from female athletes as well as in the same way sixty athletes were selected from low self-efficacy, in which thirty were selected from male athletes and thirty were selected from female athletes. The purposive sampling technique was used for the selection the sample. Their age range between was 20 to 26 years. In this study self-efficacy scale has developed by, Dr. A. K. Singh and Dr. Shruti Narain and Aggression Inventory is developed by M.K. Sultania have been used. Conclusions: It is concluded that, there is significant difference found in level of aggression among the high and low self-efficacy athletes. The athletes of low self-efficacy have found more level of aggression than athletes of high self-efficacy. Another concluded that, there is insignificant difference found in level of aggression among the male and female athletes. The male and female athletes have found equal on their level of aggression.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".