A Framework for Fire Safety Management in School Facilities
Bibliographic record
Abstract
Schools are high-risk type of facilities in fire emergencies. This requires the implementation of safety measures, which comprise administrative and operational efforts to mitigate the occurrence of fire accidents. The review of literature indicated the lack of practical approaches that can be easily adopted for safety management in school facilities. This paper presents the development and implementation of a generic framework for fire safety management in school facilities. A literature review in the domain of fire safety in schools was performed to comprehend various knowledge areas in this context. A generic framework for fire safety management in school facilities was developed. The framework was implemented in a secondary school, located in the Eastern Province of Saudi Arabia, as a case study, to illustrate its applicability. An action plan was developed to improve the level of fire safety in the school facility, under review. The findings indicated that the framework provides a methodological and systematic approach for assessing the level of fire safety in schools, as well as determine the possible actions for improving the overall safety condition in such facilities. The paper provides a useful focus for practitioners and researchers concerned with fire safety in the educational workplace.
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.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".