Examining Social Service Providers’ Representation of Trafficking Victims: A Feminist Postcolonial Lens
Bibliographic record
Abstract
As anti-trafficking social service providers (SSPs) facilitate the process of victim recovery and empowerment, they also participate in the dissemination of trafficking-related knowledge to the general public. Drawing on a feminist postcolonial framework, this study sought to examine how anti-trafficking SSPs represent trafficking victims in written narratives published on their organizational websites. Thirty-three narratives were drawn from the websites of 10 New York–based anti-trafficking SSPs. Despite the widespread adoption of a strength-based term, “survivor,” the narratives were found to reinforce a gendered and racialized representation of trafficking victims as sex trafficked women from the “global South” and to (re)produce many “ideal” trafficking victim stereotypes that have been dominating the current discourses of trafficking. A “life transformation” discourse was pervasive, discursively foregrounding the positive impact of the SSPs on trafficking survivors. The findings suggested a need for anti-trafficking SSPs to engage with critical reflection on their positionality and intentionality in representing trafficking victims/survivors and to adopt a survivor-led storytelling paradigm. This study also provided a timely reminder for social work practitioners and researchers to continue to challenge the dominant narratives embedded in their fields of practice, to exercise critical self-reflexivity, and to provide a discursive space for those who have been deprived of voices.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".