Exploration initiale des renseignements sur les incendies dans les collectivités autochtones contenus dans la BDNI (2005-2021) en fonction des données de géolocalisation : résumé des renseignements sur les incendies de structures résidentielles
Bibliographic record
Abstract
This exploratory effort was undertaken in response to the current Canadian situation of a scarcity of fire incident information for Indigenous communities and with the hope of identifying an additional data source that might enrich the information collected via the recently launched voluntary National Incident Reporting System (NIRS) [NIFSC, 2024]. Key results from the exploratory analysis include the ability to identify available fire information in the NFID for incidents that were located within Indigenous communities. Furthermore, where comparison with previously published studies was possible, similar trends were observed during the exploratory analysis of the current study for all residential fires . Therefore, the NFID could be used to provide an additional data source to combine with NIRS information so that the enriched data could be available for Indigenous communities to help inform fire risk reduction plans tailored to and by each community. For this report the definition of “Indigenous community locations” is related to the geolocation information that was reported in the NFID and how the incident information was collected. The reported geolocation information was used to identify a subset of the NFID for incidents likely occurring within an Indigenous community. When considering the results in all cases, care is cautioned in the use of the results and observations due to the following factors: • Relatively small data sizes; • Potential for under-reporting of Indigenous community incidents since mutual aid was a key contributing factor for reporting, including: o Mutual aid would be limited to locations, where an adjacent community: has an Municipal Services Agreement, or equivalent agreement, in place with the Indigenous community, and reports into the federal, provincial or territorial system participating in the NFID survey cycles; o Information included in the NFID was reported by staff of the attending fire department providing mutual aid, who were not necessarily members of the adjacent Indigenous community; and o Reporting of incident information by an Indigenous community fire department is not expected to be included in the federal or provincial collection systems. • Heavy reliance on the consistency and accuracy of reported geolocation information in combination with lists of Indigenous community names and Census Subdivision information; • Under-reported detail of individual incidents indicated by the level of unknown information; and • The information that is available cannot be meaningfully understood without considering the historical context and present day-realities of Indigenous communities. This is not to detract from the general observations from this data source, but to be considered when applying the results to specific situations or considerations. In such cases, a detailed review and analysis of the available data would be recommended to explore the available information in context for the community. For this study, residential fire incident information was identified with Indigenous community locations, based on available geolocation information, for 4,089 fire incidents; 191 fire-related civilian deaths; and 179 fire-related civilian injuries. Key observations included that results for some select variables (i.e., area of fire origin, object involved in ignition, performance of smoke alarm devices, and act or omission) might indicate similar ranked most common categories or even similar percentages when comparing the datasets for Indigenous and non-Indigenous community locations. Those observations were not applicable to all variables. Furthermore, the comparison of rates of civilian fire-related deaths to 1,000 fire incidents consistently demonstrates higher rates for Indigenous community locations compared to non-Indigenous. A few noted observations of the rates of the number of fire-related civilian deaths to 1,000 fire incidents include: • Performance of smoke alarm devices , were approximately: o 5 times higher for no smoke alarm or alarm did not activate for Indigenous community locations compared to non-Indigenous community information. • Objects involved in ignition , were approximately: o 3 times higher for smoker’s equipment and open flames for Indigenous community locations compared to non-Indigenous community information; o 9 times higher for cooking equipment for Indigenous community locations compared to non-Indigenous community information; and o 10 times higher for heating equipment for Indigenous community locations compared to non-Indigenous community information. • Act or omission , were approximately: o 12 times higher for reported human failing (e.g., including instances where a person may be asleep or fatigued; have a temporary loss of judgement; being impaired; have an accident; or be distracted or preoccupied) for Indigenous community locations compared to non-Indigenous information; and o 5 times higher for reported misuse of ignited materials for Indigenous community locations compared to non-Indigenous information. An example of noted exceptions to the general trends of similar most commonly reported for the act or omission for the category of incendiary fires with: • The 1st most commonly reported fire incident (~35%) for Indigenous community locations, compared to the 4th to 6th most common (with three categories with about the same percentage of ~12%) for non-Indigenous information; and • The rate of the number of fire-related civilian deaths to 1,000 fire incidents was 1.4 times higher for Indigenous community locations compared to non-Indigenous information. Key recommendations based on observations in this study include: • Where available, Indigenous community-related data is recommended to be used to inform the fire risk for that community; • Where the amount or quality of the available data is limited for an Indigenous community location, the use of fire information trends observed for non-Indigenous community locations are not recommended, and, if used, should be used with caution; and • The importance of data collection to support evidence-based discussions and decisions continues to be reinforced.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".