Adaptive Capacity Index of Public Schools in the Municipalities of Bay and Los Baños, Laguna, Philippines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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