Socio-cultural Impacts of Tourism on World Heritage Sites: Communities' Perspective of Lamu (Kenya) and Zanzibar Islands
Bibliographic record
Abstract
Most World Heritage Sites are major cultural tourism attractions. The majority of visitors to these sites are generally motivated by an interest in culture, nature and heritage. The high numbers of visitors that World Heritage Sites usually attract mean that issues of accessibility, transport, accommodation, other service provision, information or impacts of visitor pressure must be discussed and potential problems must be solved by appropriate management techniques. However, a conflict of interests is most likely to emerge among the various parties involved in the management of a site: governments (or tourism organizations) on a national level usually wish to use the site as a marketing tool in image creation, local people expect increased tourist flows bringing employment and income, and site managers attempt to preserve the quality of the given World Heritage Site and avoid negative impacts of visitation by restricting visitor numbers and educating visitors about appropriate behavior. Kenya has three listed locations as heritage sites, of which Lamu Old Town is one. Zanzibar Stone Town is listed as a heritage site. This article will focus on the degree of involvement in tourism planning, management and ownership of the sites as well as the socio-cultural impacts of tourism at the two sites.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| 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".