Comparison of Volleyball Skills of Rural and Urban Students in Bardiya
Bibliographic record
Abstract
This study aims to compare the volleyball skills of rural and urban secondary school students in Bardiya district. A descriptive research method was employed, and respondents were selected using a random sampling technique. The AAHPER volleyball skill test, a standardized assessment tool, was utilized, comprising four specific skills: volleying, serving, passing, and setting. To analyze the data, various statistical techniques were applied as required. Each test item was compared separately between the rural and urban groups. The findings indicated that the mean scores of rural school students were higher for all four skills. A Z-test was conducted to determine the significance of the differences, revealing significant differences in each skill item. The study concluded that rural secondary school students in Bardiya district possess better volleyball skills compared to their urban counterparts. This conclusion is based on the higher mean scores and significant differences observed in the AAHPER volleyball skill test items. These findings suggest that rural students may have more opportunities or better conditions for developing their volleyball skills compared to urban students in this region. The findings of this study may be helpful to the coaches and selectors of players of volleyball game to find new players who possess potentiality to be good player of the game.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".