Sociological Perspectives of Anti-trafficking Organizations: A Case Study of The Salvation Army
Bibliographic record
Abstract
Human trafficking is one of the world's fastest-growing, international, organized crimes.Further, there is a plethora of organizations that are working to end human trafficking.One organization is The Salvation Army.The Salvation Army is a unique case study as it is an international faith-based organization operating in 134 countries.The overarching research for this project is: How does a faith-based organization such as The Salvation Army name and frame the question of human trafficking?I argue that the re-framing of The Palermo Protocol within The Salvation Army highlights the intersection of international policy and religious beliefs in addressing the issue of human trafficking.By expanding the Traditional 4P Framework, The Salvation Army can address the complexities of human trafficking through a lens of moral obligation and faith-based principles.This re-framing is only a part of anti-trafficking efforts but challenges the dichotomy between international policy and religious perspectives on human trafficking.Ultimately, this thesis argues that religious beliefs are important to anti-trafficking responses for faith-based organizations.The Salvation Army case study shows how The Palermo Protocol can be re-framed and understood through religious beliefs.It offers a unique and additional approach to combating this global issue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".