YARDIMSEVERLİK KOŞUCULARININ MOTİVASYONLARINI, SOSYAL İLİŞKİLERİNİ VE DENEYİMLERİNİ ANLAMAK İÇİN CİDDİ SERBEST ZAMAN KURAMININ KULLANILMASI
Bibliographic record
Abstract
Running in marathons by non-professional athletes for a cause has attracted the attention of researchers, the athletic community, and ordinary citizens in recent decades. However, studies examining these charity runners are limited. Therefore, this study aimed to understand the motivations to participate in a charity event and charity runners' experiences and social relationships within serious leisure theory. As a secondary aim, perceptions of the participants toward their charity organization’s management strategies were examined. This qualitative study collected data from 11 charity runners (7 men, 4 women) of the Middle East Technical University (METU) Alumni Association (İstanbul branch) via semi-structured interviews, participatory observation, and document analysis. Data were transfused into verbatim transcripts and then analyzed. Findings indicated that in addition to altruistic motivations, charity runners have numerous reasons to participate in charity running, including sports, socialization, and nature. These motivations are also indicators of serious leisure concept. Hence, this study was expected to expand serious leisure literature and shed light on charity runners’ leisure behaviors. Results uncovered that charity runners socialize for networking or solidarity and might either run or fundraise seriously. Results also demonstrated that charity runners intend to fund the students and research more due to insufficient public funding. Findings were expected to reorient management strategies towards charity running. Finally, the results of this study may be convenient for the effective use of funds raised.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".