Recreational Travel Decisions: Push-Pull Dynamics on College Students
Bibliographic record
Abstract
The purpose of this study is to examine the recreational tourism preferences of university students within the framework of their push and pull travel motivations. In addition, it was aimed to highlight how some variables and motivations affect tourism preferences in mutual interaction. Validity and reliability were obtained by “Scale of Recreational Activities in Destination Choices” (SRADC), “Scale of Intrinsic Travel Motivations” (SITM) and “Scale of Extrinsic Travel Motivations” (SETM) conducted by Özdemir, Karaküçük, and Büyüköztürk (2013). Descriptive statistics, independent sample t test, one way ANOVA and Univariate test were used for data analysis, for in-group comparisons Tukey (HSD-LSD) and Pearson Correlation test were used. In this study, it was determined that .85 for recreational activities in destination choices scale, .90 for SITM and .91 for SETM. While the push and pull travel motivation of the participants was above the mean values (123.96 ± 15.65; 121.35 ± 16.81), the highest subscale score in push travel motivation was obtained by the Exploring-Knowledge subdimension (39.01 ± 5.74). The highest sub-dimension score was obtained from the Escape sub-dimension (38.31 ± 5.98). In the pull travel motivation sub-dimensions, the highest sub-dimension score was the quality-atmosphere sub-dimension (39.29 ± 5.24), while the lowest sub-dimension score was obtained by the Natural Environment sub-dimension (19.98 ± 4.58). The findings of the study showed that the importance given to recreational activities was influenced by variables such as gender, year of study and perceived welfare, but also revealed differences in gender, field of study, year of study, and perceived welfare in push and pull travel motivations. In addition, there is a moderate positive relationship between recreational choices, push travel motivation and pull travel motivation. As a result, it has been determined that university students have high push and pull travel motivation and recreation preferences are differentiated between SITM and SETM by certain variables.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".