Preventing sexual harassment through a prosocial bystander campaign: It’s #SafeToSay
Bibliographic record
Abstract
Sexual harassment is pervasive and often hidden, occurring on a continuum of violence against women, domestic abuse, and sexual violence (VAWDASV), and often underpinned by problematic attitudes and beliefs. Bystander interventions have been shown to illicit positive outcomes in VAWDASV prevention. Therefore, the Wales Violence Prevention Unit created the #SafeToSay campaign, to encourage prosocial bystander responses against sexual harassment. The campaign was delivered in two phases. Phase One was delivered in Cardiff and Swansea, calling everyone to action. Phase Two was delivered in Swansea and specifically engaged men. Both phases received a process and outcomes evaluation using social media and website analytics, and a public perception survey. The surveys showed that members of the public felt that #SafeToSay had drawn people’s attention to an important issue and had provided them with some of the information and skills needed to take prosocial bystander action against sexual harassment. However, men had particularly negative responses to some of the social media advertisements in Phase Two. Possible explanations for this have been explored. When considering future iterations of #SafeToSay, more work is needed to understand what works in engaging men and boys in violence prevention campaigns through research, focused engagement, consultation and coproduction with this group. Similarly, refining the target audience, including exploring options for targeting other socio-demographics, should be considered. This could be achieved through behavioural insights work, such as surveys, interviews, and focus groups. This would support the development of messaging to make the campaign more relatable to the desired target audience.
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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 | 0.007 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".