Exploring the Coastal Tourism Potentials of Lagos
Bibliographic record
Abstract
Nigeria, just like the ASEAN countries, is in the process of metamorphosizing into a developed country. In its quest for developing other sectors of the economy to diversify from its main stay which is oil, Nigeria is looking to tourism as a possible alternative income earner for the nation. Growing statistics indicate the increasingly financial gains in exploiting the untapped wealth of coastal tourism: it is increasingly an area of interest whose potential lies hugely unexploited in Nigeria. Lagos, its former capital, is one of Nigeria’s coastal cities. Water-based sites in the city are largely neglected or grossly under-utilized thereby wasting their natural recreational potentials. This research seeks to examine the existing water tourism destinations, identify the problems causing lack of popularity, and subsequently proffer solutions enabling policy makers in government and private sector plan better. Data were collected through the administration of structured questionnaires and interviews from sixty randomly selected users and industry practitioners in Tarzan Jetty, Ozumba Mbadiwe Waterfront, Bar Beach Harbour and the Marina Waterfront. Data collected were analyzed using descriptive statistics and mean item score. Result of the survey showed that all the four water-based tourist destinations experience lack of infrastructure, most especially functional ferries or other water transport, piers, canoes and boats for pleasure rides and sightseeing, properly designed areas for relaxation and passive leisure, lack of security and non availability of restaurants, shopping facilities and conveniences. The provision of these infrastructures will definitely improve the current state of coastal tourism in Lagos.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".