SOAR as an Effective Community-based Response in Anti-Trafficking Movements
Bibliographic record
Abstract
Grounded in the narratives of women from rural communities who were forced to migrate to Kathmandu, the capital of Nepal, and later India, this paper critically examines the meaningful involvement of trafficking survivors for sexual exploitation in anti-trafficking movement in Nepal. Using the SOAR (Stop, Observe, Ask, and Respond) model, this paper explores the community-based responses to address the issues of human trafficking and post-trafficking. This paper is guided by migratory and intersectionality frameworks. Using the frameworks, Participatory Action Research (PAR), a transformative and an empowerment methodology, was conducted with eight female trafficking survivors who were exploited for sexual exploitation. PAR was used to critically understand intersectional gender oppression escalated the vulnerability of women to trafficking and made the women “doubly victimized” in their post trafficking. Through engaging in the study process, PAR allowed survivors to critically understand their own oppression and develop strategies to effectively act towards ending forced migration and trafficking. Using a thematic analysis, the collected information was categorized, and coded. The research team included the researcher and the trafficking survivors, who are recognized as “co-researchers” in this paper, identified and used a wide range of pragmatic approaches and tools such as street dramas, interactive sessions, peer interviews and meetings with political leaders. These approaches provided the survivors with an opportunity not only to share their voices and experiences on migration and trafficking, but also to highlight transformative impacts, including personal and social transformation.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".