The Politics of Combating Human Trafficking in the United States
Bibliographic record
Abstract
This book examines political responses to the problem of human trafficking, including proposals, actions (legislative and executive), and statements made by politicians, government agencies, and civil society organizations to solve or mitigate the crime of human trafficking. The objective is not just to recognize the nature and impact of human trafficking but to understand the approaches used or recommended to solve the problem and the motivations behind such strategies. The issue of human trafficking has become particularly important given the charged environment regarding border politics. The book details the various policy options that have been proposed, supported, opposed, or passed by US politicians over the past five to ten years. This includes decisions made by presidents, legislators (national and state), agencies, and interest groups. Court decisions on human trafficking policies and media coverage of events are also explored. This political analysis is designed to help readers understand what motivated the proposals designed to address human trafficking and the impact those policies had or are having. This book is ideal as a primary text for college courses in human trafficking and modern slavery or a supplemental text for a range of scholarly courses of study, including human rights, criminal justice, law, and political science. It is recommended for anyone with an interest in human trafficking and what might be done to stop it.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".