All for one? Collective responsibility and psychological climate
Bibliographic record
Abstract
While attributing responsibility for an outcome has a long history in psychology (Heider, 1958), it has received much less attention in sport. This is surprising given that teams are typically lauded for their 'all for one mentality', yet, games are often ostensibly decided by the play of one or a subgroup of players. Further, if the perception is that all members are collectively responsible for a losing outcome, then what does that say about team climate? This study explored whether perceptions of differing levels of responsibility (individual or collective) by members would be associated with different perceptions of psychological climate (PC) within a sport team. Curlers (N=66) completed online measures of PC (Spink et al., 2012) and collective responsibility (designed for this study) near the end of their season. For the analysis, individuals were split into two responsibility groups: 1) those who reported that individuals were more responsible for losses (individual responsibility, n=29), and, 2) those who reported that the team was more responsible for losses (collective responsibility, n=37). A MANOVA was performed with responsibility as the IV (individual vs collective responsibility) and the PC subscales as the DVs. Results revealed that PC differed across the levels of responsibility, F(4,61)=2.616, p=.044, etap2=.15. Post hoc analysis revealed that self-expression was the PC subscale that significantly differed between conditions (F(1, 64) = 4.61, p=.036). Those who perceived that it was primarily the team (versus individuals) who was collectively responsible for the loss reported being more able to express themselves around the team.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".