Adaptive Capacity Index of Public Schools in the Municipalities of Bay and Los Baños, Laguna, Philippines
Bibliographic record
Abstract
An instrument to measure School Adaptive Capacity Index was developed using livelihood assets and school management as the main determinants using the theory driven approach to indicator development. Randomly selected teachers from the 38 public elementary and high schools from Bay and Los Baños Laguna, grouped according to the effects of floods experienced, were interviewed. It was found that the schools in general were highly adaptive. High schools have better human and physical assets than elementary schools, while non-flooded schools have better natural assets than flooded schools. SACI of high schools were significantly higher than elementary schools. On the other hand, flooded and non flooded schools have more or less the same SACI. School management and social assets were vital in increasing the adaptive capacity of schools in the different groups. Scores in a particular asset may vary between groups and within each group implying that there is no uniform approach to improving the adaptive capacity and that interventions should always consider the uniqueness among each of these schools. The instrument developed is highly recommended to assess the institutional adaptive capacities of other schools to floods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".