The cohesion-effort relationship in competitive and recreational youth soccer
Bibliographic record
Abstract
Sport team cohesion has been associated with a number of positive adherence behaviours (e.g., attendance and punctuality; Carron et al., 1988, Study 2). However, considering contextual factors concomitant with this relationship may be important (cf. Ulvick et al., 2011). Although different dimensions of cohesion have emerged between elite and recreational adult athletes (Brawley et al., 1988, study 2), this finding has not been extended to youth. To examine the cohesion-adherence relationship across two different youth sport contexts (competitive and recreational), 132 players were recruited from 10 outdoor soccer teams. Approximately 2-3 weeks into their season, players completed measures of team cohesion (YSEQ; Eys et al., 2009) and individual effort as a form of adherence (Spink & Odnokon, 2000). Regression analyses were conducted for the competitive and recreational players separately. While both the competitive, F (2, 66) = 17.26, p < .001, R2 = .34, and recreational, F (2, 60) = 3.70, p = .03, R2 = .11, models were significant, different cohesion dimensions predicted effort. For those on competitive teams, task and social cohesion were related to effort (both p’s < .01), whereas only social cohesion was related to effort for recreational players (p < .05). These results suggest that, while cohesion may be related to how hard players work, this relationship appears to differ across levels of competitive play. One possible explanation for this difference may involve the varying participation motives of each group (Klint & Weiss, 1986). Future research investigating motives in relation to cohesion could be fruitful.Acknowledgments: This research was supported by a SSHRC Doctoral Canada Graduate Scholarship and 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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".