Using Cultural Resources in Host Communities and the Environs to Enhance the Natural Resources Attractiveness of Ecotourism Development in Omo Biosphere Reserve (OBR), Nigeria
Bibliographic record
Abstract
The study was undertaken to evaluate the cultural resources in Omo Biosphere Reserve (OBR) host communities and its environs that can enhance the attractiveness of ecotourism development which can ultimately lead to the sustainable development of the area. OBR is in waterside area of Ogun State, Nigeria. It is a UNESCO designated Man and Biosphere (MAB) site. OBR natural resources has been under threat of overexploitation in the last few decades and hence the need to sustainably conserve and protect these resources using ecotourism. Fauna direct sighting particularly the large mammals like the forest elephants (Loxodonta africana cyclotis), forest buffalo (Syncerus caffer nanus) and others have been rare although their presence have been established. Interview and direct observation methods were used to collect primary data. Data collected were analysed and presented in tables. The result showed that there were significant festivals in OBR environs that can attract visitors to OBR. The study identified the incorporation of festivals in the OBR as added attractiveness for the natural resources in OBR in its ecotourism development. Development of ecotourism will not only conserve the endangered fauna and flora species of Omo Biosphere Reserve but will also provide additional income and job opportunities for the communities and will equally ensure peaceful co-existence of the people.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".