The socio-economic potential of agro tourism in Trinidad and Tobago at the micro-level
Bibliographic record
Abstract
The Government of Trinidad and Tobago depended heavily on taxes and royalties from the oil industry as their revenue earner. Oil contributed about 50% of the GDP. Lately there has been considerable drop in oil prices from USD104 to USD 40- 68 per barrel. This has put Trinidad and Tobago in a recessionary state. This has resulted in the poverty level increasing to 20% in a once oil rich country. The research looked at the socio economic potential of agrotourism with the research objectives being a critical analysis of the target audience and their contribution to the socio economic potential for agrotourism, identifying the opportunities, enablers and the problems for the development of agrotourism and to identify the social and economic potential of agrotourism at the micro-level of farming communities. The research philosophy took the form of a pragmatism epistemology and a subjective ontology. The research approach was inductive. This led to a multi-strategy using both qualitative and quantitative methods at different times. Data was collected from the tourists, the farmers and the extension officers by means of a questionnaire and from other main stakeholders by interviews. Analysis was done using thematic analysis, SPSS and Excel. Through the findings, a profile was developed based on the tourist typology of Plog (1972) and Cohen (2001) and the motivation model by Bansal and Eiselt (2003) of the target audience visitors from USA, Canada, and Caribbean. The opportunities identified for agrotourism were iv farm visits and stays, bird watching, picnic areas, farmers market, picking of fruits, herbal medicine, culinary delights and village life. The socio-economic potential of agrotourism to the marginalised rural communities would generate income by the sale of crafts and culinary delights, farmstays, visits and the provision of services. This income would lead to a better social life where they can afford better quality of life and therefore less dependency on the government for social services. There would also be higher agricultural production to meet the needs of the visitors. Village life would be improved because of the interaction with the foreigners. There would also be revival of lost traditions in crafts and culinary arts. Younger persons would stay on the farms rather than migrate to the urban areas because of income, safety and employment. Entrepreneurship would be an integral part of the village life and so sustainable living. Agrotourism then becomes a potential to improve the livelihood of farming communities at the micro-level in for Trinidad and Tobago.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".