From Vulnerability to Empowerment: Critical Reflections on Canada’s Engagement with Refugee Policy
Bibliographic record
Abstract
The making and implementation of global policy are prominent areas of activity for the global refugee regime, with a specific focus on policy relating to the categories of vulnerable refugees. Recent collective efforts globally have highlighted the importance of meaningfully including refugees themselves; and a discursive shift away from the language of vulnerability towards that of empowerment in policy making, and humanitarian assistance. Despite this, efforts to implement these commitments have largely been unsuccessful, raising questions about how refugees are engaged in these processes, and in what ways the label of vulnerable continues to influence the making and implementation of global refugee policy. Using the case of Canada’s engagement with the global refugee regime, and with refugee women in particular, this article argues that the continued framing of refugee women as vulnerable has impeded progress, and that for transformative policy to be realized, refugee women must be seen as actors with capacity to participate, and must be included in all processes of policy making, implementation and evaluation. A feminist geopolitical framework is presented as a way to decenter states and institutions in favor of centering the individual embodied experiences of refugee women in global refugee policy making. By doing so, empowerment can be realized in policy and practice.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.113 | 0.072 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.014 | 0.024 |
| 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".