Do Years of Running Experience Influence the Motivations of Amateur Marathon Athletes?
Bibliographic record
Abstract
The aim of the study was to investigate if years of running experience influence the motivations of marathon athletes. An empirical study was conducted during the last (20th) PKO Poznan Marathon, one of the largest and most popular mass running events in Poland, which was held in Poznan (Poland) in October 2019. A total of 493 marathon runners (29% of whom were female, and 71% of whom were male) took part in the cross-sectional study, which used the diagnostic survey method. The questionnaire employed the division of motives from the motivation of marathoners scale (MOMS) by Masters et al., adapted to the Polish language by Dybala. Running motivations have already been analysed for variables such as age, gender and place of residence, but there is a research gap regarding existing research, as the relationship between motivations and running experience has not yet been studied. One-way analysis of variance for independent samples was used to verify statistical hypotheses. Prior to making the relevant calculations, the assumption of homogeneity of variance was checked via Levene’s test. Variances were assessed with an F-test, and if they were unequal, Welch’s correction was applied. Eta squared (η2) was used as a measure of effect size. The calculations carried out showed that running experience was not a statistically significant factor in the motivations of runners taking part in a marathon.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".