The influence of demographic factors on travel behaviour of visitors to nature-based products in South Africa
Bibliographic record
Abstract
Nature-based products in South Africa are playing an increasingly important role in attracting visitors to the country. It thus becomes more important to understand the travel behaviour of visitors as this can influence future development and marketing strategies to these products. However information in this regard is lacking which creates challenges in the sustainable development of nature-based products. It is therefore the aim of this paper to determine the influence of demographic factors on travel behaviour of visitors to nature-based products in South Africa. A survey was done in 2010 which included nine National Parks in South Africa resulting in 1300 questionnaires. A factor analysis on travel motivations revealed five factors with the highest mean value obtained for „relaxation?. A second factor analysis for park experiences also revealed five factors with the highest mean value obtained for „activities and facilities. A t-test for Equality of Means was calculated for age, home language, presence of children and province, and revealed significant differences on both travel motivations and park experiences. Most differences exist on Relaxation and Learning for travel motivations and Maintenance for park experiences. An ANOVA was done on qualification and travel motivations and park preferences and revealed only one significant difference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".