An analysis of livelihood linkages of tourism in Kaziranga National Park, a Natural World Heritage Site in India
Bibliographic record
Abstract
We evaluated the livelihood linkages of existing tourism practices in Kaziranga National Park, a World Heritage site located in Assam, India.The main objective of the study was to assess the contribution of tourism to local livelihoods and suggest ways to strengthen these linkages.Focus group discussions and interviews of tourism service providers were carried out to identify their share of tourism income.A survey of tourists was conducted to examine the amount spent by visitors while visiting the park.The primary data was supplemented by secondary information obtained from the park office, service providers and records of village self-help groups.In 2006-2007, the total amount of money that flowed through the tourism sector in Kaziranga National Park was estimated to be US$ 5 million per annum, of which different stakeholders (excluding government) received US$ 3.27 million per annum.The balance of income flowed as leakage for purchase of supplies and logistic support outside the tourism zone.The financial benefits to local stakeholders may increase if the leakages could be prevented through planned interventions such as proper marketing of products from cottage industries and strengthening of local level institutions.In addition to wildlife viewing, promotion of nature trails and package tours may be encouraged in the buffer zones and adjoining forests areas to enhance tourist visitation to un-tapped sites that could provide additional livelihood options to local communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".