A test of the team identification – social psychological health model: Does being a fan of a local team matter?
Bibliographic record
Abstract
The team identification-social psychological health model (Wann, 2006) proposes that highly-identified sport fans will experience greater feelings of social connectedness only if the teams they support are based locally. We conducted two studies to test this proposition and did not find any evidence to support it. In Study 1, undergraduate sport fans (N = 291) completed an online survey and reported their favourite team, their level of fan identification, and the extent to which they derive social connections from their fandom. Teams were coded as being either local or non-local based on geographical location. In Study 2, we replicated the design of Study 1 with a sample of sport fans recruited online (N = 432). But rather than assess team localness based on geographical location, we asked fans to report the extent to which they perceived their favourite team as being a local team. In both studies, we found that team identification was associated with greater feelings of social connectedness (Study 1: r = .334; Study 2: r = .542). This positive association, however, was not moderated by team localness in either study. These results mean that the social benefits of being a highly identified sport fan are not limited to those who support local teams. We speculate that advances in technology (e.g., social media, live-sport streaming) now allow modern sport fans to have many virtual ways to establish connections with others, regardless of where the teams they support are located.Acknowledgments: Research Manitoba; SSHRC
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".