Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)
Bibliographic record
Abstract
The Philippines is a country where a quarter to one-third of the population\nis poor. Although the nation has managed to lower poverty incidence\nin some years, its booming population increases the poor population dramatically.\nThis is why alleviating poverty is a pinnacle program in the\ncountry.\nIn aid of poverty alleviation endeavor, this study focuses on assessing\nwhich programs had been effective in alleviating poverty given other\nfamily characteristics. Aside from descriptive methods, employing Generalised\nLinear Models (GLMs) and categorical data analysis are the focus\nin analysing the effects of existing intervention programs on status of\nimprovement and income of families. In addition, varying effects of programs\ndepending on values of other covariates are also analysed.\nDescriptive analysis and modeling are applied on the panel data of\nfamilies. Intervention programs namely scholarship, Comprehensive Agrarian\nReform Program (CARP) and government housing or other housing financing\nprogram (GHFP) have been run together with other family characteristics\nto describe improvement in welfare and income. Interaction\neffects, between access to intervention programs and other aspects of the\nfamily, have been derived to give a richer picture of the phenomenon. The\nstudy has come to conclude that the programs are indeed effective in improving\nlives of families, with some effects varying on some levels of other\nexplanatory variables.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".