“You Can Sit in the Middle or Be One of the Outliers”: Older Male Athletes and the Complexities of Social Comparison
Bibliographic record
Abstract
Sporting events for older adults are proliferating in both popularity and participation numbers, mirroring the growth that is occurring globally with an aging population. Preliminary evidence indicates that older athletes have a tendency to compare themselves (in terms of their performance, participation, and aging) to inactive older adults deemed ‘worse-off’. Using social comparison theory as a guiding framework, our aim was to explore the way athletes spoke about their own sport participation and the extent to which that influenced their perceptions of other people in their age cohort. We also interpreted their stories in the broader context of current policies which promote sport across the lifespan. For this study, 17 male competitive athletes (age range from 70 to 90 years) who participated in either the 2013 or 2017 World Masters Games were interviewed as part of a larger project on the meaning of sport in their lives. Seven different sports were represented, and participants hailed from multiple countries. We used qualitative methods to interview each participant, analyze individual transcripts, and develop common themes across the data set to address the aforementioned aims. Two major themes emerged from the analysis, Sport as social comparison: “It’s the competitive nature” and Downward comparisons. A number of participants commented on the nature of sport, and competitive sport in particular, as being important to their motivation to train and prepare. This results-oriented environment appeared to provide a fertile ground for social comparison. Within the theme of Downward comparisons, we established two categories; Resisting loss, and Assigning blame. While downward comparisons were used by our participants to separate themselves from other seniors of the same age, thereby bolstering their sense of self, they also tied those comparisons to individual responsibility for health and believed that compromised health was due to individual negligence and bad decisions. Ultimately, the useful psychological strategy of social comparison for maintaining a positive sense of self and performance may also have some unintended individual and societal consequences.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".