Migrant Rights in International Law: Exploring the Gendered Experiences of Migrant Women and Girls
Bibliographic record
Abstract
Migrants are people who choose to move from one place to another to seek a better life. However, when these people move across international borders as a result of fleeing from a ‘well-founded fear of persecution’ and not based on choice, they are forced migrants. This article examines the situations when women and girls move across international borders in order to flee from persecution or massive violations of human rights linked to gender violence. The article argues that women and girls experience vulnerabilities at all stages of the migration cycle not only because they are forced migrants fleeing from life-threatening situations, but also as a result of violations of their human rights based on gendered experience. An examination of the Convention on the Elimination of All Forms of Discrimination Against Women and the Immigration and Refugee Board of Canada’s Chairperson’s Guideline No 4 shows that, when these instruments are misapplied or not considered, the vulnerabilities of migrant women and girls may be exacerbated by such misapplication or non-consideration. This article ends by concluding that a focus should not be upon a woman or girl’s vulnerability, but on her agency to be self-reliant and resilient towards her own destiny.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".