Motivation-based transformative learning and potential volunteer tourists: facilitating more sustainable outcomes
Bibliographic record
Abstract
Transformative learning (TL) is an important component of sustainable volunteer tourism experiences, potentially reducing unsustainable outcomes, and educating and enlightening volunteers. This paper reviews theories and issues about TL in volunteer tourism, and analyzes data from 1008 useable responses to an online survey of potential volunteer tourists. A factor–cluster analysis of potential volunteer tourists’ motivations identified key volunteer tourist segments and assessed differences in expectations of TL across each segment. Altruism remains the primary motivation, with personal development an expectation, but the study also found desires to experience different cultures, build relationships with family, and to escape one's daily life. Three motivation segments emerged: Volunteers, Voluntourists, and Tourists. Differences in the three clusters’ expectations for TL were assessed through multiple analysis of variance using items representing Taylor's three elements of TL: self-reflection, engaging in dialogue, and intercultural experience.Differences in TL expectations varied significantly across the three segments. Potential Voluntourists were most likely to expect to participate in TL opportunities. The paper concludes with suggestions for maximizing TL for each segment. Volunteers and Tourists may require activities that include different, less obvious forms of TL. Volunteer tourism organizations need to invest significantly in staff training in TL.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".