A Call to Anger : A feminist participatory approach to anti-trafficking communication for social change in collaboration with the Youth Advocacy Group of NGO Atina
Bibliographic record
Abstract
Voices of women affected by trafficking in human beings for sexual exploitation (THBSE) are often marginalized and they are not usually included in the design and implementation of anti-trafficking campaigns. Although these campaigns present themselves as Communication for Social Change, the final result often betrays the principles of this form of communication. This thesis aims to tackle that gap, in partnership with the Serbian NGO Atina and their Youth Advocacy Group. The objective is to understand what is the perspective of women affected by THBSE in regards to institutional imagery of anti-trafficking campaigns and its persuasive potential for social change. The methodology was informed by a feminist participatory approach which included a) a focus group where participants were shown examples of anti-trafficking imagery, b) a workshop where participants designed their own campaign, and c) two expert interviews. After analyzing a sample of 21 images of anti-trafficking campaigns from 12 countries (Serbia, USA, Canada, France, Spain, Portugal, Romania, Brazil, Luxembourg, Colombia, Ukraine and Israel), this thesis finds that anti-trafficking campaigns tend to fail at their potential for social change. This is due to misrepresentations that ignore the complexity of THBSE as a system of exploitation and by perpetuating harmful stereotypes about what a victim should or should not look like, thereby contributing to the silencing and marginalization of victims. Anti-trafficking campaigns also tend to rely on awareness-raising rather than promoting social change. This thesis also presents what a campaign designed by women affected by sexual exploitation would look like, which focused on systemic change rather than individualized narratives. Based on the research, it was also possible to propose guidelines for participatory approaches to communication against THBSE.
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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 | 0.041 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".