Using social identity mapping to understand the implications of student-athlete group compatibility
Bibliographic record
Abstract
Social Identity refers to the extent to which people define themselves based on the groups to which they belong (Tajfel, 1981). Despite an extensive body of literature spanning several fields of psychology, most research on social identity has involved membership with only one group. Considering that humans are a social species, often belonging to several groups concurrently, investigating implications for multiple memberships is warranted. The purpose of this study was to explore social identity compatibility in interuniversity athletes, with a specific interest in assessing associations with quality of life and academic and athletic performance. A total of 235 interuniversity athletes (57% female; Mage = 19.98 years; SD = 1.77) completed an online social identity mapping tool (oSIM; Cruwys et al., 2016), and questionnaires assessing the criterion variables. Results indicated that two mediation models were statistically significant. First, general health quality mediated the relationships between the proportion of very incompatible groups and individual perceived athletic performance. Second, a specific dimension within health quality, emotional health quality, also mediated the relationships between the proportion of very incompatible groups and perceived athletic performance. These results are discussed in terms of both their theoretical and practical implications within the thesis. However, as a generally summary, our findings suggest the importance of experiencing harmony among group memberships, and that this is particularly important in populations with overt group affiliations. Within the thesis, limitations are also acknowledged (i.e., cross-sectional research design) and suggestions are made to promote future research using the novel online oSIM tool in sport.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".