We win together, we lose together: Effect of group constructs on collective responsibility
Bibliographic record
Abstract
While sport teams are often lauded for their "all-for-one" mentality, the reality is that games are often lost by the play of one member. Are all members collectively responsible or is the loss attributed to the individual? Further, do group factors affect the way members attribute team failures? Both cohesion (e.g., Brawley et al., 1987) and the perception of groupness (e.g., Denson et al., 2006) have been associated with members assuming collective responsibility for different events. However, little is known about how consideration of these constructs together impacts these decisions. To answer this question, adult soccer players (N = 69) read four vignettes describing hypothetical soccer teams that differed in levels of cohesion (high[HC] vs. low[LC]) and groupness (high[HG] vs. low[LG]). While imagining themselves as a member of each of the four hypothetical teams, participants were asked to report whether the individual or the team would assume responsibility for three scenarios where a teammate mistake resulted in a loss. ANOVA results revealed a significant main effect, p < .001, ηp2 = .33. Post-hoc tests revealed significant differences between conditions (all ps < .01, .57 < Cohen's d < 1.08). Collective responsibility was highest after reading the vignette describing the HC/HG team, followed by the HC/LG and LC/HG (which did not differ), and LC/LG teams. These results provide preliminary evidence that group-level constructs (i.e., cohesion, groupness) influence athletes' allocation of responsibility in team sport. Further, it appears that perceptions of cohesion and groupness have independent and additive effects on collective responsibility.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".