Safe Kids Week: Analysis of gender bias in a national child safety campaign, 1997–2016
Bibliographic record
Abstract
Background and Purpose Child safety campaigns play an important role in disseminating injury prevention information to families. A critical discourse analysis of gender bias in child safety campaign marketing materials can offer important insights into how families are represented and the potential influence that gender bias may have on uptake of injury prevention information. Methods Our approach was informed by poststructural feminist theory, and we used critical discourse analysis to identify discourses within the poster materials. We examined the national Safe Kids Canada Safe Kids Week campaign poster material spanning twenty years (1997-2016). Specifically, we analyzed the posters’ typeface, colour, images, and language to identify gender bias in relation to discourses surrounding parenting, safety, and societal perceptions of gender. Results The findings show that there is gender bias present in the Safe Kids Week poster material. The posters represent gender as binary, mothers as primary caregivers, and showcase stereotypically masculine sporting equipment among boys and stereotypically feminine equipment among girls. Interestingly, we found that the colour and typeface of the text both challenge and perpetuate the feminization of safety. Discussion It is recommended that future child safety campaigns represent changing family dynamics, include representations of children with non-traditionally gendered sporting equipment, and avoid the representation of gender as binary. This analysis contributes to the discussion of the feminization of safety in injury prevention research and challenges the ways in which gender is represented in child safety campaigns.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".