Bibliographic record
Abstract
This paper explores the positive effects and factors that shape collective identities within voluntary sport clubs (VSCs). As the role and development of collective identities in VSCs remain poorly understood, Classic Institutional Theory is combined with Social Identity Theory to identify the micro- and organisational-level factors that shape these identities. A multiple case study is performed among four Dutch voluntary tennis clubs. Results show significant positive relationships between members’ level of organisational identification and three indices of member involvement (loyalty, volunteering and positive WOM). Given that VSCs rely heavily on member involvement for their functioning, this suggests that both providing club goods that align with members’ interests and fostering a collective identity are crucial for their long-term survival. Furthermore, to understand how collective identities are fostered, this study examined which social groups are key in shaping collective identities within VSCs and identifies the organisational factors that trigger organisational identification processes. Results show that embeddedness in social relationships and various social groups within VSCs, shape collective identities and spur organisational identification among members. Governmental policy aimed at strengthening VSCs should therefore focus on imbuing VSCs with identity cues and stimulating the formation of social relationships between members.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".