From Minor Literature to Diasporic Literature Immigrant Women in Suzan Samancı's Kurdish Stories
Bibliographic record
Abstract
Suzan Samancı began her literary career by writing in Turkish, and the fact that the content of her works is about Kurds has led to her being categorized as a minor writer. However, since Samancı has repeatedly expressed her desire to write in Kurdish, Kurdish literary circles have also argued that she should be included in Kurdish literature. Samancı emigrated to Europe and put an end to these debates by writing in both Kurdish and Turkish. She is now a Kurdish literary figure writing from the diaspora. Not only the language but also the content of Samancı’s literature has changed. Although women and their problems were the focus of her works when she was writing in Turkish, she emphasizes the female identity more strongly in her Kurdish works. In almost all of these stories, immigrant women and their problems are at the center of the narratives. Based on these discussions, this study aims to first examine the changes in Samancı’s literary life and then describe how immigrant women are portryed in her Kurdish stories. According to the results of the study, the immigrant women in the stories are often exposed to violence from their partners and struggle with racism while dealing with language and integration problems. The immigrant women have also realized that Europe, where they have come to seek freedom, is a false dream.
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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 | 0.002 |
| 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 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".