Landmark Articles From Volumes 31-40 / Des articles-jalons tirés des volumes 31 à 40 - A Narrative Study of Refugee Women Who Have Experienced Violence in the Context of War
Bibliographic record
Abstract
Although women are rarely on the frontlines of battle, as in many other realms of contemporary life they bear a disproportionate burden of the consequences of war. Many have experienced torture firsthand or been witnesses to the torture or killing of family, friends, and loved ones. The use of rape and other forms of sexual torture has been well documented. For those who are forced to flee their homes and countries, separation from spouses, children, and other family members is common. Because of the sheer magnitude of global conflict, the number of refugees and displaced persons throughout the world has risen exponentially. It has been estimated that women constitute more than half of the world's refugee population. The purpose of this narrative study was to examine the experiences of refugee women who experienced violence in the context of war. Data analysis revealed 8 themes: lives forever changed, new notions of normality, a pervasive sense of fear, selves obscured, living among and between cultures, a woman's place in Canada, bearing heavy burdens - the centrality of children, and an uncaring system of care. Implications for research and practice, including limitations associated with individualized Western approaches, are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".