Human trafficking across the Americas: Survivors, services, and the law
Bibliographic record
Abstract
Human trafficking has garnered increasing attention and global awareness as a significant violation of fundamental human rights. This modern-day slavery is actively occurring both internationally and in our local communities. The Trafficking in Persons (TIP) Report outlines the extent and typical services allocated for survivors of human trafficking by country. Additionally, the report details the funding allocated toward prevention and services, as well as the annual efforts of each country’s government to meet the minimum standards outlined by the Trafficking Victims Protection Act (TVPA, 2000). This study aimed to examine the chronological growth and decline of specific government efforts to combat human trafficking in countries across North, South, and Central America. Specifically, descriptive differences in trends and services were explored country by country, comparing the narratives provided in 2014 to those offered in the 2018 TIP report. The overall trends indicate that almost two-thirds of the governments of countries in the Americas have remained relatively unchanged in their efforts to comply with the minimum standards of the TVPA (TIP, 2014; TIP, 2018). Only three countries have consistently remained in the top tier (i.e., Canada, the United States of America, and Chile).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".