Heritage Tourism The Way Out For Rural Poor? A Case Study Of The Tourism-Poverty Nexus In Anse La Raye, St. Lucia
Bibliographic record
Abstract
Tourism has emerged as one of the most dynamic sectors in many countries; as one outcome, it has generated widespread hope that this particular industry can bring prosperity to numerous developing nations. Conversely, there is substantial suspicion of its capacity to bring equitable benefits to the poor. A recent proposition is to strengthen the tourism-poverty nexus by placing tourism at the heart of poverty reduction strategies. This thesis explores the application of this new and relatively untested approach. It does so by conducting a case study of the impact of heritage tourism, a community-based and poverty-focused tourism initiative, implemented in Anse La Raye, the most impoverished rural community in the Caribbean island of St. Lucia. Obviously a single case study cannot justify sweeping generalizations, but it can perhaps serve at least to raise a number of useful policy questions that might also have some broader application.The findings reveal that poverty-focused tourism development initiatives can positively impact the lives of the rural poor, under certain circumstances. Some observable effects included the creation of useful community infrastructure, linkages of direct and indirect employment benefits and consequent income generation. Notwithstanding these successes, this limited research piece suggests that, despite their nearly exclusive and commendable focus on the livelihood of the poor, pro-poor approaches to tourism also have limitations [for example, seasonal and part-time employment, and leakages] and certainly cannot be regarded as a panacea for reducing poverty in any poverty stricken region. Nonetheless, it is believed that St. Lucia, at least, can learn from the experiences of Anse La Raye as it further refines its tourism development policies in quest of further development targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".