Young People’s Perspectives on Safety and Safety Promotion in St. John’s, Newfoundland and Labrador
Bibliographic record
Abstract
Safety is an essential public good, more so in urban locales, and it has become imperative to understand safety in the city from the perspectives of diverse city dwellers. This paper explores young people’s perspectives on safety and its promotion in the city, using a group of young people in St. John’s, Newfoundland and Labrador (NL). The study answers the question, what are young people’s perspectives on safety and safety promotion in the city? A conceptual framework on safety promotion and discourse analysis methodology informed the design and conduct of the study. Following purposive sampling, 23 young people aged 15 to 24 years participated in face-to-face interviews and data were analyzed deductively and inductively to identify themes. Three themes emerged which constitute participants’ safety discourse as follows: 1. Safety as essential for health and wellness—portraying safety as mental, physical, and social health; 2. Safety as threatened by behavioral and environmental factors; and 3. Safety promotion as personal and public responsibility—where individuals must act responsibly and leaders must invest in public safety measures. Consistent with the character of discourse, participants constructed safety as fundamental to human health, frowned upon behaviors and practices that threaten safety, and allocated responsibility for safety promotion. Male and female participants alike shared the view that safety is essential for health and wellbeing, and that promoting safety should be the collective responsibility of individuals and public leaders. On threats to safety, although female and male voices were represented, male participants were predominant in identifying factors related to traffic violations, criminal activity, and other forms of public nuisance. Findings of this study have implications for the empirical literature and for practice. The findings indicate that young people have a nuanced understanding of safety and are able to provide relevant suggestions on how to promote safety in the city. Their safety discourse identifies important threats to safety which, if addressed, will promote individual and collective feelings of safety. Consistent with what is known of discourses, the safety discourse aims to sanitize the community environment, to monitor and discipline individual behavior, and to shape institutional policies and practices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".