Assessing Pro-Poor Tourism principles in practice: Ethnic tourism in northern Thailand and Lao PDR.
Bibliographic record
Abstract
Pro-Poor Tourism (PPT) is promoted by certain international organizations as an approach to tourism that benefits poor people and contributes to the United Nations' Millennium Development Goals for global poverty eradication. It is premised on a set of principles that have yet to be adequately tested on the ground for their attainability. Emerging critiques suggest that PPT is based more on rhetoric than reality. In a case study of ethnic tourism in northern Thailand and Lao PDR, I assessed the feasibility of putting PPT principles into practice. Assessment was defined as identifying, primarily through interviews, challenges to achieving the principles in the study region. I documented 13 major challenges and extrapolated implications for PPT as a global strategy. My results indicate that PPT is more of a livelihood supplement than a poverty solution, and that significant hurdles must be overcome before large-scale poverty elimination through ethnic tourism can be realized.--P.ii.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".