Forays into the Backstage: Volunteer Tourism and the Pursuit of Object Authenticity
Bibliographic record
Abstract
Volunteer tourism is a form of travel that combines traditional leisure pursuits with opportunities to volunteer in an organized fashion. The popularity of volunteer tourism stems from many factors, but the one motivation that appears in virtually every study is a desire for object authenticity, defined as the authenticity of toured objects, people, and settings. The purpose of this paper is to explore the role played by object authenticity in the motivations and experiences of volunteer tourists in the province of Chiang Mai in northern Thailand. Based on interviews with 62 volunteers and 15 directors, managers, and staff members from volunteer tourism organizations based in Chiang Mai, this paper argues that volunteer tourists conceive of object authenticity both as a package of cultural stereotypes focused on authentic people, and as authentic backstage settings where ‘real’ Thai reside. Aside from demonstrating that the desire for object authenticity is the central motivation for international volunteers in northern Thailand, this study indicates that the pursuit of object authenticity is complicated by language barriers, the potential staging of authenticity on the part of locals, and the need to balance familiarity with alterity in the carefully selected ‘voluntourscapes’ in which volunteer tourism takes place.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".