Sensation Seekers as a Target Market for Volunteer Tourism
Bibliographic record
Abstract
The purpose of this study was to determine if sensation seeking and consumer innovativeness are useful characteristics in identifying a productive target market for volunteer tourism offerings. Sensation seeking and consumer innovativeness are trait characteristics describing needs for new experiences, risk taking, simulation, and consumer willingness to integrating these needs into their consumption of products and services. Extreme sports enthusiasts, thought to be high sensation seekers, were surveyed. Chain-referral methods were used to recruit the sample. Findings indicate that respondents were high in sensation seeking and consumer innovativeness. Many also expressed a desire for future volunteer work. Findings indicate that respondents would be a potential target market for volunteer tourism experiences and suggest that certain individual traits can be useful in identifying other individuals that would be a productive target market for volunteer tourism offerings. A better understanding of the benefits this group desires can have implications for approaching this group. The area of volunteer tourism is relatively new and underresearched. Investigating whether or not high sensation seekers represent a potential market for volunteer tourism has not been previously researched.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".