Intensifying Insecurities: The impact of climate change on vulnerability to human trafficking in the Indian Sundarbans
Bibliographic record
Abstract
Despite an enormous amount of attention paid to the factors that shape vulnerability to human trafficking, such as poverty and a lack of economic opportunity, the debate of evidence for what enables these factors to exist in the first place is relatively less explored. Presently, discussions of the relationship between climate change and human insecurity have been marginal to broader debates about vulnerability to trafficking. This paper argues that this signifies a gap in our understanding of the underlying drivers that push individuals and communities into situations where vulnerability to trafficking amplifies, but also that increase the pull of risky migration pathways and exploitative work situations. This paper proceeds by examining and problematising dominant conceptualisations of vulnerability in human trafficking and climate change discourses. Next, it presents a case study of the Sundarbans region of India to highlight how climate change impacts compound and exacerbate the same factors that shape vulnerability to human trafficking—including environmental degradation, loss of livelihood, destitution, and forced migration. Lastly, it argues for enhanced attention to climate change-related insecurity as evidence of vulnerability to trafficking and outlines what such insights can bring to anti-trafficking efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".