An examination of nonverbal behaviours in successful and unsuccessful professional volleyball teams
Bibliographic record
Abstract
The purpose of this study was to explore the relationship between nonverbal communication and team success in real-time professional volleyball games. The sample included the top four and bottom four teams from the Turkish Men’s Volleyball 1st League from the 2016–2017 season. The development of a coding scheme for nonverbal behaviours (NVBs) was informed by the extant literature and interviews with volleyball experts (n = 5). Video recordings of 24 matches were analysed under three conditions for each team (a win, a loss, and a tie-break game). The findings indicated that successful teams displayed a greater amount of NVBs in total, and used significantly more instructional and supportive NVBs than their less successful counterparts. In addition, successful teams demonstrated more frequent use of instructional NVBs during the games that they won, more supportive behaviours when they lost, and both of these behaviours during tie-break games. Results from the present study highlight the different uses of NVBs between successful and less successful professional volleyball teams, which has both theoretical and practical implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".