National Survey Identifies Disaster Preparedness Gaps in Philippine Libraries
Bibliographic record
Abstract
A Review of: Superio, D. L., Yap, J. M., Sebial-Guinanao, J. M. L., & Calilung, R. P. (2024). When a disaster strikes: Are libraries in the Philippines ready? IFLA Journal, 50(2), 322-340. https://doi.org/10.1177/03400352231222039 Objective – To assess the level of disaster preparedness and management practices of Philippine libraries. Design – Web-based survey questionnaire. Setting – Online survey conducted between March and May 2019. Subjects – Ninety head librarians or officers-in-charge of academic (52), school (24), public (8), or special (6) libraries in the Philippines. Methods – Participants were recruited online by convenience and snowball sampling. Invitations were posted on the Facebook pages of various library associations and councils and shared through personal posts and messages. Main Results – Thirty-nine (43%) of the respondents indicated that their libraries had experienced at least one natural or human-caused disaster between 2009 and 2019, including earthquakes (18%), floods (18%), typhoons (16%), and fires (10%). However, only 21 (23%) of the surveyed libraries had a formal disaster management plan (DMP). Limited financial (51%) and human (41%) resources were the most frequently identified constraints for the lack of DMP. Even so, most libraries did employ some preparedness measures, such as fire and theft alarms (63%), emergency kits (59%), or scheduled trainings or drills (46%). Conclusion – Noting the limited capacity to prepare for and respond to disasters at most Philippine libraries, the researchers called for systematic interventions by national and local government agencies and library associations to provide the necessary resources and training to improve knowledge around and capabilities for disaster resilience across all types of libraries.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".