Unpacking Human Trafficking from Neoliberalism and Neoconservatism Paradigms in Nepal: A Critical Review
Bibliographic record
Abstract
This theoretical review paper examines the trafficking of women and children in Nepal caused by oppression and socio-economic marginalization and unpacks human trafficking from neoliberal and neoconservative paradigms. It does not discuss human smuggling but instead provides a critical examination of the forces contributing to human trafficking in Nepal according to the neoliberal and neoconservative paradigms. It begins with a brief overview of human trafficking in Nepal and then explores the international frameworks related to human trafficking. It then briefly examines the “4 P” strategy – prevention, protection, prosecution and partnerships – related to anti-human trafficking efforts and identifies gaps in practice/policies. It concludes with a critical discussion of the implications for social work. The paper also stresses that anti-trafficking intervention programs and approaches must be accountable and responsive to the aspirations, strengths, wisdom and experiences of the specific community and be sensitive to the external and internal forces contributing to the trafficking they seek counter. It claims that there is a need for participatory action research that invites trafficking survivors to engage in critical dialogue and conversation and help develop integrative strategies to address human trafficking in Nepal. To write this paper, the author critically reviewed secondary data, including qualitative and quantitative studies and NGO publications, but does not claim to provide a comprehensive or systematic analysis of evidence.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".