“Haitian Paradox” or Dark Side of the Security-Development Nexus? Canada’s Role in the Securitization of Haiti, 2004–2009
Bibliographic record
Abstract
Drawing on analysis of government records obtained using Access to Information Act requests, the author examines the securitization of Canada’s aid program to Haiti between 2004 and 2009. The author discusses how Canadian agencies, including the Royal Canadian Mounted Police (RCMP), Correctional Service of Canada (CSC), and the Canadian International Development Agency, were involved in capacity-building initiatives that focused on police reform, border surveillance, and prison construction/refurbishment across Haiti in the aftermath of a coup that ousted the democratically elected President Jean-Bertrand Aristide. The author demonstrates how these efforts at securitization resulted in what officials referred to as the “Haitian Paradox,” whereby reorganization of the Haitian National Police force led to higher arrest rates and jail bloat, creating conditions that violated rather than ameliorated human rights. While the securitization project may have been based on the rule of law and human rights in Canadian policy makers’ official discourse, in practice these securitization efforts exacerbated jail overcrowding, distrust of police, and persecution of political opposition. The author therefore demonstrates one way that international development, aid, and criminal justice intersect, with emphasis on the transnational aspects of RCMP and CSC activities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".