Users’ Knowledge of Fire Safety Measures in Educational Environment: A Case Study of a College Building in Nigeria
Bibliographic record
Abstract
This study investigated students' knowledge of the fire safety measures of a college building in Nigeria, in order to identify areas for improvement, towards contributing to ways of enhancing effective fire safety management through users' involvement in educational environments.The research is a case study that adopted quantitative research approach.Data was gathered from 153 students with the use of a closed-end structured questionnaire that was analysed with SPSS software.The findings were presented descriptively with the aid of tables.The results indicated that most of the students' lacked knowledge of some basic fire prevention and protection measures which limits their knowledge of the fire safety measures and protocols of the college building.Likewise, most of the participants are either not certain of their views or not aware of the locations of the fire safety signs, and fire exits of the building.The study emphasised the need for regular fire drills and trainings on fire safety management procedures for regular users of academic environments in conformity with best practice.The study underscored the need for users of public environments, especially educational settings, to be knowledgeable in basic fire safety protocols in order to be able to rightly align with laid down precautions for saving lives and properties in the event of a fire outbreak.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".