Our State / Ourselves: Discourses on Sexual Exploitation and Abuse in Police Peacekeeping
Bibliographic record
Abstract
This paper presents findings from interview research with Canadian police officers deployed to the UN peacekeeping missions in Haiti between 2004 and 2017. Focusing on the problem of sexual exploitation and abuse (SEA), we present three discourses that emerge from this research and their reasoning about the problem of SEA. These discourses suggest that (1) other contributing countries are responsible for the problem of SEA; (2) the UN fails to sanction SEA in practice, while Canada does sanction SEA; and (3) Haitians and Haitian culture undermines efforts to reduce SEA. Using tools of critical discourse analysis, we show how discourses on SEA reinforce a mentality of self-exemption that treats sexual misconduct as a problem in which Canada and Canadians are largely innocent, while the UN, other contributing countries, and Haitians themselves, bear much more fault. We argue that these discourses reproduce a narrative of innocence and contribute to Canada’s national mythology as a do-gooder nation that is largely exempt from perpetrating SEA, despite evidence to the contrary.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.044 | 0.050 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".