Conceptualizing affective and emotional responses to inclusion and exclusion: The case of subgroups in sport teams
Bibliographic record
Abstract
Feeling included is essential for achieving core social motives such as self-esteem (Fiske 2004) and as such, individuals closely monitor their integration within groups. Although sport teams provide opportunities to experience inclusion, they may nevertheless be a context for teammates to exclude one another. The purpose of this presentation is to explore the affective and emotional responses associated with inclusion within (and exclusion from) subgroups of athletes who form close bonds within teams. First, we re-analyzed qualitative transcripts generated through recently published research involving 22 interuniversity athletes (Martin et al. 2015) and 21 elite level coaches (Martin et al., under review) who reflected on their experiences with subgroups in sport. Through targeted thematic analysis, several themes emerged which were then paired with existing literature (e.g., Williams, 2007) to conceptualize affective and emotional experiences in relation to experiences with subgroups. Through this process, we formulated several postulates regarding responses to inclusion within, and exclusion from, subgroups in sport teams. Consistent with previous research (e.g., Allen & Hecht, 2004), positive affective responses emerged in descriptions of inclusion within subgroups. Individuals who felt excluded from subgroups, on the other hand, reported distinct emotions according to the situation and the individual. This corresponds with literature involving the diverse emotional reactions to exclusion (i.e., anger, guilt, and shame). This presentation will explore how these postulates may generate future research, with the intention of further understanding how to facilitate optimal experiences within sport teams.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".