Imperialist Consequences and Contemporary Crisis: A Comparative Analysis of Human Trafficking in Ukraine and Canada
Bibliographic record
Abstract
Human trafficking is a highly intersectional problem as the victim pool, perpetration, and assumptions regarding these dynamics are largely intertwined with positions of power, race, gender, and other social factors. This paper will focus on power in the form of imperialism as a critical extenuating factor for trafficking. Imperialism is the aggressive enforcement of one nation onto another, which can include colonialism, systemic oppression of minorities, and increased victimization of those not part of an imperialistic force. This paper compares Canadian and Ukrainian students' perceptions of the hypothesized relationship between imperialism and human trafficking, specifically within these countries. By elucidating how power disparities aid in recruiting, transporting, and transferring individuals for exploitation translates to a better assessment of how to educate and prevent future trafficking cases. Interviews with research participants supplemented the literature, creating a consensus linking colonial/imperialistic forces and the heightened risk Indigenous populations face within Canada. Similarly, research underscored how the current war in Ukraine, a contemporary example of imperialistic invasion, has translated into an increase in human trafficking cases. These findings substantiate the correlation between imperialism and human trafficking. This connection should be further exhausted with a larger sample size to further illuminate human trafficking crime trends and the bias that denies justice to specific populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".