How are Canada’s Humanitarian Policies Supporting Rohingya Sexual Violence Survivors?: Reflections of Canadian Government and NGO Workers
Bibliographic record
Abstract
Canada introduced the Feminist International Assistance Policy (FIAP) in 2017 to support and empower women and girls affected by violent conflicts worldwide. Canada’s feminist approach to humanitarian engagement is a commitment to addressing sexual gender-based violence, one of the most severe and pervasive forms of human rights violations during conflicts, through advocacy and financial investment. Following Myanmar’s brutal persecution of the Rohingya Muslims in August 2017, Canada, like many other countries, provided humanitarian relief. In 2018, Canada introduced the first Rohingya Response Strategy for Myanmar and Bangladesh, committing C$300 million to international assistance until 2021. The Canadian government renewed the strategy for a second time in 2021 until 2024, committing an additional C$288.3 million. Canada’s humanitarian assistance for the Rohingya in Bangladesh has provided life-saving services, including food, shelter, sexual and reproductive health services, and psychological support for women and girls who experienced sexual violence at the hands of the Myanmar military. To meet the long-term needs of the Rohingya in Bangladesh, the Canadian government has also supported education and skill-building projects. My research builds on the central question: How is Canada supporting the needs of Rohingya sexual violence survivors through its humanitarian assistance programming in Bangladesh? I use gender mainstreaming as a theoretical framework and use thematic analysis to analyze the qualitative data collected from interviews with key informants. I triangulate them with the existing literature to answer my research question. My findings indicate that for its humanitarian programming to support Rohingya sexual violence survivors effectively, Canada needs to go beyond the “one-size-fits-all” approach. To do so, the Canadian government must consider the context-specific needs of women and girls. My findings also indicate that Canada needs to start advocating for the needs of male sexual violence survivors, who are often overlooked in global humanitarian response. My findings reinforce that Canada needs to integrate the expertise of grassroots organizations and the voices of women and girls to ensure a more inclusive, efficient, and sustainable response to the Rohingya crisis. My findings are expected to lead to a broader conversation within the Canadian government to design policies that contribute to long-term and sustainable solutions to address the needs of survivors of conflict-related sexual violence globally.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.087 | 0.022 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".