Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)
Why this work is in the frame
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Bibliographic record
Abstract
<p>The Philippines is a country where a quarter to one-third of the population is poor. Although the nation has managed to lower poverty incidence in some years, its booming population increases the poor population dramatically. This is why alleviating poverty is a pinnacle program in the country. In aid of poverty alleviation endeavor, this study focuses on assessing which programs had been effective in alleviating poverty given other family characteristics. Aside from descriptive methods, employing Generalised Linear Models (GLMs) and categorical data analysis are the focus in analysing the effects of existing intervention programs on status of improvement and income of families. In addition, varying effects of programs depending on values of other covariates are also analysed. Descriptive analysis and modeling are applied on the panel data of families. Intervention programs namely scholarship, Comprehensive Agrarian Reform Program (CARP) and government housing or other housing financing program (GHFP) have been run together with other family characteristics to describe improvement in welfare and income. Interaction effects, between access to intervention programs and other aspects of the family, have been derived to give a richer picture of the phenomenon. The study has come to conclude that the programs are indeed effective in improving lives of families, with some effects varying on some levels of other explanatory variables.</p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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