Determinants of Wellbeing Among Smallholders in Adjumani District, Uganda
Bibliographic record
Abstract
An ordered logistic regression model was used to empirically establish the quantitative effects of community identified (local) determinants of wellbeing on the level of household wellbeing. The model was fitted to data for a sample of 200 households collected in the last quarter of 2002. The dependent variable, poverty category, has three levels namely poorest =1, Less poor =2, and Better off =3. Fourteen independent variables are used. Results show that households that own less than 5 acreage of land, that are male headed, have a nonagricultural source of income and are actively involved in agricultural development activities have a higher probability (odds) of enjoying wellbeing above any given level. Land ownership seems to be the most important determinant of wellbeing in Adjumani district. Furthermore, owning livestock and having a household head with an education level of secondary school and above are also important determinants of household wellbeing in Adjumani district. We find household wellbeing to be negatively affected by household size, age of the household head and whether any family member has had any long illness although only the age of the household is significant. We recommend deepening of the Universal Primary Education (UPE) and initiation of Universal Secondary Education to increase the education levels of the rural people. We also recommend continued and expansion of community level agricultural development activities, strengthening of the land tenure provisions to enhance access to land and initiation of programs to enhance animal ownership among small holder farmers in Adjumani.
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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 | 0.001 |
| 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".