Examining the interactive effects of cohesion and descriptive norms on the individual effort of youth soccer players
Bibliographic record
Abstract
Effort is a valuable attribute in sport teams. Two variables that have been positively associated with players’ effort are team cohesion and descriptive norms. For example, athletes who feel more cohesive with their teammates (Carron et al., 1985) have been found to work harder (Prapavessis & Carron, 1997). As well, perceptions about how hard teammates were working (i.e., descriptive norms) have been linked with individual players’ self-reported effort (Spink et al., 2013). While studies have examined cohesion and norms independently in the sport setting, no research has considered how the combination of team cohesion and descriptive norms for effort might interact to affect individual effort. Thus, the purpose of the current study was to examine this relationship in a sample of youth soccer players. During the last two weeks of their season, 156 players from 10 intact teams (M = 13.3 years, SD = 1.1) completed measures of task cohesion (YSEQ; Eys et al., 2009), descriptive norms for effort (using peer nomination; Prell, 2012), and self-reported effort (Spink et al., 2013). As players’ self-reported effort responses were independent of their teammates’ (ICC < .05), hierarchical regression was used. The overall model was significant (p < .001), accounting for 21.2% of the variance in players’ self-reported effort. On step 1, task cohesion was related to self-reported effort, β = .40, p < .001, whereas descriptive norms were not (p > .05). On step 2, the addition of the interaction between cohesion and descriptive norms also was significant (p = .05). A post-hoc simple slopes analysis (Aiken & West, 1991) revealed that the positive association between cohesion and self-reported effort was strongest for those on teams with a high norm for effort. While this finding requires replication, it provides preliminary evidence that team norms for effort might moderate the cohesion-effort relationship.Acknowledgments: This research was supported by a SSHRC Canada Graduate Scholarship (Doctoral) and a SSHRC/Sport Canada Sport Participation Research Initiative grant.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".