The safe tweet: social media use by Ontario fire services
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how fire services use social media to educate the public about safety and fire prevention. Design/methodology/approach Grounded theoretical methods were employed in a rigorous qualitative analysis of five significant fire services’ Twitter accounts in Ontario, Canada. Findings Seven main themes emerged from the data, with an overarching conclusion that tweets made by fire service organisations and professionals do not focus primarily on fire safety. Research limitations/implications This paper addresses a gap in the literature in terms of understanding how social media communicates information about all three lines of defence against fire, with a focus on the first two: public fire safety education, fire safety standards and enforcement and emergency response. Practical implications The authors suggest that fire services need to employ a more segmented approach to social media posts with an objective to engage and educate the public. Originality/value This paper is the first extensive qualitative analysis to consider the particulars of fire services’ social media presence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".