The Leadership Role in Sustainable Community-Based Tourism Development Within a Rural Setting
Bibliographic record
Abstract
The development of community-based tourism has seen many communities in rural areas take control of their economic well-being, social justice issues, and environmental integrity. In many quarters, this kind of freedom has been attributed to the availability of competent leadership that can mobilise the community and the resources endowed to the community. This study investigated the role of community leadership in the development of sustainable community based tourism. The study assumed a qualitative research approach that followed an exploratory design. The chosen approach employed semi-structured interviews of 15 respondents through a non-probability sampling technique to solicit data. The collected data was analysed using NVivo software version 12, producing a comprehensive observation report accompanied by word trees, word clouds, cluster analysis, and tree maps. The study's findings produced themes indicative of the community’s views concerning the role played by community leaders in the development of sustainable community-based tourism. These findings presented orations by respondents indicating contributions of community leaders in the development of sustainable community based tourism together with the shortcomings in terms of the leaders’ capacity and skills required to lead a community away from poverty and vulnerability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".