Economic aspects of primate tourism associated with primate conservation
Bibliographic record
Abstract
Introduction This chapter considers primate tourism as a form of wildlife tourism, that is tourism based on encounters with non-domesticated animal species (Higginbottom, 2004), stressing encounters in their natural environment and intended to be non-consumptive. The rapid growth of wildlife tourism around the world is influenced by many different groups, including tourists, tour operators, local communities, conservation organizations, and governments. These groups are involved for a variety of motives, including recreational enjoyment, business development, community development, protection of wildlife and their habitats, and tax revenues. All these motivations have economic aspects (Lindberg, 2001). Consider a few examples. Recreation enjoyment is substantial and can be valued monetarily; such benefits can rival or exceed those of other types of land uses. Tourist expenditures can stimulate local development, such as transportation or communications infrastructure, that can benefit local residents and tourists. Tourism revenues can raise funds for wildlife conservation projects, provide local residents with alternatives to less sustainable resource uses, and support governmental and non-governmental educational goals. Tourism-related businesses can support various levels of government by generating tax revenues, and businesses that receive tourist expenditures will, in turn, re-spend some of that money in the local region. Other economic benefits from wildlife tourism include local employment, industry stimulation, economic diversification, and infrastructure improvements (McNeely et al ., 1991). On the other hand, economic costs result from wildlife tourism, notably in establishing and controlling tourist facilities and services. They may grow if, for example, tourist expenditures increase inflation or tourist activities harm the wildlife, natural habitats, or regions visited.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".