Integrating the Role of Sports Associations in the Promotion of Sports and Recreation Tourism at the Destination Level: Creating a Partnering Framework for Kenya
Bibliographic record
Abstract
In Kenya, tourism is the second most important earner of foreign exchange after agriculture. It has had a great impact in the direct employment of local population as well as in generating opportunities for other business activities such as accommodation, food service, transport, retail, and other auxiliary services. In the recent past, Kenya’s tourism has faced numerous challenges which may have slowed tourist arrivals and growth considerably. The challenges have been driven by climatic and environmental changes that maybe interfering with the ecosystem. Kenyan tourism faces significant challenges also from intense regional competition, political instability, poor governance, corruption, negative travel advisory by the governments of source markets, poor security at the destination, dilapidated infrastructure within the destination, poor product/ service innovation, and inadequate market and customer value perception of the destination. In this regard, a need to examine possible ways of reinvigorating and diversifying Kenya’s tourism offerings has emerged and one area that holds a great potential is Sports and Recreation tourism. This paper therefore carries out an exploratory assessment of the awareness of the members of the various sports associations regarding the significance of sports tourism to Kenya’s economy. Club representatives duly registered within ten selected sports associations that represent popular sports in Kenya were surveyed to achieve this objective. This research proposes an integrated approach to the creation of a partnering role amongst sports associations to help promote sports and recreation tourism in Kenya.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".