The effects of age, gender and level of experience on motivation to sea kayak
Bibliographic record
Abstract
Although motivation to participate in outdoor leisure activities has been the frequent focus of research, there are no studies that examine motivation to sea kayak. The purposes of this study were two-fold: (1) to examine the differences in motivation to sea kayak based on the interaction of age and gender, and (2) to examine differences between paddlers of different levels of experience. Participants included 176 paddlers recruited from four sea kayak symposia. Participants were asked to complete an 84-item version of the Recreation Experience Preference (REP) scales as well as to provide demographic information. Data were analysed using a series of analysis of variance tests. Results indicated that men of various age groups were motivated differently by temperature. Additionally, men were more motivated than women to sea kayak by using equipment, taking risks and teaching/leading others, while women were more motivated than men by creativity and enjoying nature. In terms of age differences, younger sea kayakers were more motivated by achievement/stimulation and escaping personal/social pressures than their counterparts in older age groups. More experienced sea kayakers were significantly more motivated by nostalgia and escaping family than were less experienced paddlers. These results may be of interest to outdoor recreation organizations providing sea kayak experiences as they shed initial light on why different groups of people are motivated to sea kayak. Programme delivery, marketing, and instructional techniques may be modified based on these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".