“Like a Tree Without Leaves”: Syrian Refugee Women and the Shifting Meaning of Marriage
Bibliographic record
Abstract
There is a growing body of feminist scholarship that highlights aspects of agency and empowerment of the refugee woman, mostly through citing examples of women challenging patriarchy and cultural norms. Extending the latter, I use a decolonizing framework to examine how refugee women strive for autonomy and empowerment through acceptingthose norms and utilizingthem strategically. In doing so, I reveal a more complex relationship between agency and victimhood and how they relate to other notions such as empowerment, vulnerability and traditional gender roles. I use the case of Syrian refugee women who marry for refugeto explore how their stories challenge Western liberal feminist views that often stigmatize similar arrangements as exploitation, sex trafficking, and/or forced marriages. The narratives of those women move beyond highlighting instances of agency, resistance, and empowerment as subversion to question the Eurocentric conceptualization of such notions. The objective of this study is three-fold: (a) reporting on and giving context to an under-research phenomenon such as marriage for refuge; (b) rethinking and challenging liberal feminist understanding of concepts such as agency, empowerment, traditional gender roles, and marriage; and (c) making the case for the potential contribution a decolonizing approach could bring to refugee research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".