Survey of risks in property security and emergency management in the educational context: case study at Maple Bear Canadian School Porto Velho
Bibliographic record
Abstract
Property security in the educational context is one of the fundamental pillars for ensuring the physical and psychological integrity of students, employees, visitors, and for preserving institutional property. In private educational institutions, where there is a high flow of people and sensitive material assets, the adoption of effective preventive and reactive strategies is imperative. This article presents the results of a case study conducted at Maple Bear Canadian School, located in the city of Porto Velho, Rondônia, aiming to identify structural, procedural, and technological vulnerabilities in the institution's property security system. The study adopts the ABNT (2018) standard as a technical reference, structuring the risk analysis in a systematic manner and in line with international standards, which makes it applicable and replicable in medium and large educational institutions. Based on situational diagnoses and critical analysis of existing protocols, it was possible to identify substantial gaps in the coverage of the video surveillance system, inconsistencies in access control procedures, as well as the absence of a properly structured and tested evacuation plan. The results obtained demonstrate that the integration of technological solutions into security management, combined with the continuous training of internal staff and the adoption of a clear institutional security policy, can promote a more resilient, secure, and prepared school environment to deal with emergencies. We conclude that property security in schools should be treated as an integral part of organizational governance, requiring investments not only in infrastructure but also in strategic planning and an organizational culture focused on prevention.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".