Conspiracy Theories and Human Trafficking: Coercive Power, Normative Ambiguity and Epistemic Uncertainty
Bibliographic record
Abstract
Conspiracy theories have become a prevalent feature of public discourse, with human trafficking featuring as a key component of contemporary populist conspiracy claims. This paper examines the impact of trafficking conspiracy theories and concludes that rather than challenging the accounts of relevant powerful actors, they reinforce the dominant narratives and institutions that shape anti-trafficking policies, and rather than democratizing knowledge, they undermine such efforts. I further argue that trafficking conspiracies are not simply symptomatic of normative and epistemic uncertainty, but actively elevate the coercive power of the state by undermining normative and epistemic sources of constraint. Ultimately, conspiracy theories on trafficking seek to control borders and police sexuality, to protect powerful actors from criticism, and to marginalize the voices of those most affected by trafficking and anti-trafficking initiatives. I conclude that countering conspiracy theories requires normative clarification in the realm of human trafficking and a commitment to inclusive, transparent, and firm epistemic foundations. The paper supports progress toward UN Sustainable Development Goal 5, by empowering women and girls – particularly marginalized women and girls – to contribute to public discourses on trafficking and Goal 16 by challenging discourses and institutions that contribute to unjust, violent and exclusive societies.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".