Increasing player effort in the youth sport setting: Perceptions of team unity and how hard teammates are working
Bibliographic record
Abstract
Why might young athletes work hard in their team setting? From a group perspective, previous studies have linked individual effort to both team unity (Ulvick & Spink, 2014) and the perception that others on the team are working hard (descriptive norms; Spink et al., 2013). As those findings came from independent, non-experimental studies, the purpose of the current study was to explore how cohesion and descriptive norms would influence self-reported athlete effort when they were examined together using a between-subjects experimental design. Fifty-five female volleyball players (M=15.4 years, SD=.6) were randomly assigned to read one of four vignettes about a team varying in cohesion (high vs. low) and descriptive norms for teammate effort (high vs. low). Participants then rated how hard they would work if they were a member of the described team. Results from an ANOVA indicated that reported effort differed across the four conditions, F(3, 48)=3.89, p=.01, ?p2=.20. Post hoc analyses revealed main effects for both team constructs. Participants who read about a team described as high in cohesion reported that they would work harder than those whose team was described as low in cohesion (p=.009, Cohen's d=.72). Participants' reported effort scores also were higher for those who read about most players on the team working hard as compared to few players working hard (p=.05, Cohen's d=.49). In addition to supporting previous independent relationships, these findings provide initial evidence that, when considered together, perceptions about team unity and teammate effort both contribute to young athletes' self-reported effort levels.Acknowledgments: This research was supported by a Social Science and Humanities Research Council (SSHRC) Canada Graduate Scholarship-Doctoral and a SSHRC/Sport Canada Sport Participation Research Initiative grant to the first author.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".