Perceived changes in the quality of terrestrial, coastal, marine environment and natural resources in coastal communities in the Caribbean
Bibliographic record
Abstract
Economies and livelihoods in the Caribbean strongly depend on the terrestrial and marine natural resource base. There are critical gaps in quantitative data and the contextual and compositional factors affecting perceptions of changes in ecosystems and natural resources. This paper used interview data from 441 households in Belize, Dominican Republic, and Suriname to explore the contextual and compositional determinants of perceptions of change in the quality of the terrestrial, coastal/marine environment and natural resources as a composite variable. The outcome variable comprised changes in fishing effort, marine and coastal environment, forest cover, agricultural land area, and soil fertility, and relationships with predictor variables were explored using univariate, bivariate, and three multivariate models. At the univariate level, most respondents in Belize and Suriname reported perceiving a significant increase in fishing effort (i.e. days and fuel spent at sea) and a decrease in forest cover, quality of the coastal/marine environment, area of agricultural land, and fertility and productivity of soils. Sex, marital status, household size, and years of experience were not significantly associated with the terrestrial variables and fishing effort. Sex was only significantly associated with the quality of the marine environment. At the multivariate level, the elderly, high school education, and household incomes were associated with a lower perception of the outcome variable. It is concluded that policy and interventions should strongly consider ethnicity and community, and improve education and household incomes to enhance perceptions of changes in the quality of the terrestrial and coastal/marine environment in the studied countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".