Analysis of Academic Performance and Challenges of Pantawid Pamilyang Pilipino Program (4P’s) Beneficiaries
Bibliographic record
Abstract
One of the government solutions to eradicate poverty and reduce the case of dropout students in school is by providing the needs of these families through cash conditional transfer program also known as Pantawid Pamilyang Pilipino Program (4P’s). This mixed method research using sequential explanatory design sought to analyze the academic performance and challenges of thirty-nine (39) Pantawid Pamilyang Pilipino Program (4P’s) beneficiaries of Collat Integrated School in Masinloc Zambales during school year 2024 – 2025. The academic performance of 4P’s students were rated “satisfactory” in five core subjects in the first quarter of the school year. Using focus group discussion as the main data gathering tool, the study yielded the following results: the top two common success stories include that 4P’s implementation brought significant change in their lives and increase sense of self-empowerment. While, common challenges in the implementation include recipients have limited understanding about 4P’s program and still struggling in financial matters. Furthermore, teacher-respondents revealed the actions that they have undertaken in the implementation of the 4P’s program which include strong parental involvement of parents in school activities and strict monitoring of school participation of 4P’s beneficiaries. In light with the findings, the researcher recommended meaningful engagements and collaborations between 4P’s student-beneficiaries, parents and school personnel towards ensuring the program continues to have a positive outcome. Furthermore, teachers are encouraged to study further on what effective learning strategy based on the needs and interest of the students to keep them in school until they finish their academic year.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".