Kanada'dan dönen eğitimli Türk göçmen kadınların deneyimleri.
Bibliographic record
Abstract
Social science research has been slow to incorporate the international migration of skilled and educated women, and the impacts of their return migration. At the same time, Turkish female migrants have been negatively stereotyped in the literature. This exploratory and descriptive study aims to address these gaps by examining the impacts of emigration and return migration on the social and work lives of educated Turkish women who have returned to Turkey from Canada. Oral history interviews were conducted with six working-age, educated female returnees in Istanbul and Ankara between February and April 2007. Aside from some common features, the six women in this study differ greatly in terms of age, marital status, field of study and work, length of time in Canada and Turkey, and the opportunities and resources available to them throughout their migrations. From the interpretive examination of the women’s narratives, patterns in their subjective social and work life experiences emerged. The issue of gender was found to pervade all aspects of the women’s lives at all stages of their migrations as they negotiated their often contradictory social roles as mothers, wives, daughters, and professionals. This study also reveals that none of the women migrated as an individual actor. Rather, contextual and stratification factors such as marital status, family configuration, language skills, prior exposure to different cultures, socio-economic background, education and labour force participation were found to shape and influence their initial potential for migration, as well as the processes and outcomes of their migrations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".