Mitigating the Scale, Reach, and Impact of Human Trafficking at Major Events: a North American Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human trafficking is a global problem with challenges for societies and those agencies tasked with the protection of the public. Much human trafficking is in the form of sex or labor trafficking with over 22,000 cases reported in the US alone in 2019. Although prevalent in many industries, the event industry is particularly vulnerable to human trafficking with tourism destinations and the major events they host prone to such activity. This exploratory study, underpinned by collaboration theory, adopts a qualitative approach by interviewing stakeholders in the US to identify the causes, scale, reach, and impact of human trafficking in the context of major events. The study then identifies those initiatives designed and implemented to help mitigate the occurrences of human trafficking activity and minimize the damage to individuals and negative reputation for those major events caught up in this modern-day criminality.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it