CHALLENGES OF ECOTOURISM AND POVERTY ALLEVIATION IN SOUTH AFRICA
Bibliographic record
Abstract
The basis of ecotourism is associated with poverty alleviation across the world. That is, ecotourism is believed to be significant for the generation of income, improvement of the standards of living of people, sustainability of the environment, political enablement of local societies as well as for educational purposes. In South Africa, ecotourism is believed to be flourishing and as a result, it is seen as a great strategy towards poverty alleviation. The aim of the study is to investigate the challenges of ecotourism towards poverty alleviation. Theoretically, the study will adopt the desktop study from which data will be obtained by means of literature review through scholarly articles, newspapers and government documents. Ecotourism is believed to be associated with various challenges which are environmental deterioration, inability to contribute to local economy as, well as cultural exploitation and deterioration. The challenges of ecotourism delay the process of poverty alleviation. Therefore, a balance between development and the environment should be created. There should be promotion of sustainable development and stewardship of the environment. Regardless of the challenges ecotourism has, the paper concludes that ecotourism positively contributes towards poverty alleviation in South Africa. Key Words: Poverty Alleviation, Ecotourism, Environmental Sustainability and South Africa revert
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".