Tourism Governance in Transition Period: Restructuring Kenya's Tourism Administration from Centralized to Devolved System
Bibliographic record
Abstract
This study sought to examine the efficacy of tourism administration within Kenya's devolved system of government. The study was based on content analysis of official documents and websites from the national and county governments in Kenya. Nine counties out of the 47 were further purposively selected for analysis of the tourism functionalities undertaken by the county governments. Data were analysed using thematic data analysis based on predetermined research questions. The study found out that whereas the role of both levels of government in Kenya's tourism development is clearly identified, gaps exist that would hamper coordinated development of a competitive tourism industry in the country. This study thus identifies a number of lessons for tourism administration restructuring for destinations transiting from centralized to decentralized government system including the need for a clear delineation of tourism development functions between national and devolved government units, institutional and functional alignment between national and devolved government levels, a clear coordination mechanism between the tourism development mandates of the two levels of government, need to standardize tourism administration and development institutional framework and functions across the devolved units, and the need for capacity building of the devolved units during the transition point.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 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".