Determinants of forest and tree uses across households of different sites and ethnicities in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
This study examines the determinants of forest and tree-product uses in rural households across three sites of different proximity to roads and forests in the Chittagong Hill Tracts region in Bangladesh. A structured questionnaire survey was conducted with 300 households of different ethnic groups, located in three different locations (remote, intermediate, on-road), to collect information on their forest and tree use during 2015–2016. We gathered information on household socioeconomic characteristics (family size, education level of head of household, size of farmland), location (three sites), and ethnic affiliation. By conducting a series of logistic regression modeling, we analyzed the key determinants that would explain the variations in forest use in the households. We recorded twelve different forest and tree products used in the households, primarily for subsistence purposes and cash income. Fuelwood, vegetables, and fish were recorded as the most important forest-sourced products used by people, regardless of socioeconomic condition, location context, and ethnic affiliation. Household land/farm size, location, and ethnic background explained significant variations in the use of forest and tree products (mainly timber, fodder for livestock). The greater the size of the landholding, the more likely timber was used for both subsistence and cash income, but the less the reliance on other products (fuelwood, thatch grass, vegetables). Our findings suggest that the location and ethnic characteristics of the rural households are important for understanding the diverse needs for forest and tree use, and should be factored into the site-specific management and sustainable use of forest and tree resources in Bangladesh and other tropical developing countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it