Socio-Cultural Conservation Strategies and Sustainability of Community Based Tourism Projects in Kenya: A Case of Maasai Mara Conservancies
Bibliographic record
Abstract
The purpose of this study was to determine how social cultural conservation strategies influence the sustainability of community based tourism projects in Kenya. The objective of the study was to assess the extent to which social-cultural conservation strategies influence sustainability of community based tourism projects in Kenya. The study used descriptive survey research design and adopt a mixed methods approach anchored on pragmatism as its philosophical underpinning. The study was conducted in two conservancies in Maasai Mara; Naboisho conservancy and Olare Motorongi conservancy. The study made use of questionnaires, interviews, participant observation as well as document analysis to collect data. Qualitative data was analyzed using content analysis while quantitative data used multiple regression analysis to test the nature and strength of the relationship between variables based on observed data and to predict the value of the dependent variable based on the value of the independent variable. With r = 0.891, r2 = 0.794, F (1, 204) = 787.02, p = 0.001 < 0.05] it was concluded that social cultural conservation strategies had a significant influence on the sustainability of community based tourism projects. The study recommends that since the culture of the Maasai community has been a tourist attraction, the older members of the community should teach and ingrain the cultural values of their community to their children. When this is done properly, there will be less danger of the younger members adopting other cultures at the expense of their rich culture.
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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 | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".