Tourism, protected areas and development in South Africa: views of visitors to Mkambati Nature Reserve
Bibliographic record
Abstract
Tourism to areas of natural beauty has recently been seen as one of the fastest growing \ninterests in many countries around the world. This brand of tourism also encompasses \ncelebrating and sharing with tourists the uniqueness and diversity of different cultures in \nareas visited. Through a case study of current tourism trends in Mkambati Nature Reserve \non theWild Coast, where an ambitious ecotourism project under the auspices of the Spatial \nDevelopment Initiative (SDI) is planned, this paper attempts to emphasize the role played by \nlocal visitors in making ecotourism a success or failure. The study found that local tourists \nare currently the majority of visitors to Mkambati Nature Reserve. It also shows that while \nunimpressed with infrastructure and other services, local tourists are prepared to spend \nmoney to enjoy the quietness that is offered by protected environments. However, local \ntourists to Mkambati are not as enthusiastic about visiting and sharing experiences in \nadjacent rural areas. If based on current trends, then planning of ecotourism ventures \nshould, at least initially, be based on local visitors’ patterns.
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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 | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".