Bibliographic record
Abstract
ii Prior to September 11 2011, Canada was recognized as a leading advocate of international refugee protection and the third largest settlement country in the world. University educated refugees were admitted to the country in part on the basis of their education, but once in Canada their credentials were often ignored. The purpose of this study was to explore, through a transnational feminist lens, immigrant and settlement experiences of refugee female teachers from Yugoslavia who immigrated to Canada during and after the Yugoslav wars; to document the ways in which socially constructed categories such as gender, race, and refugee status have influenced their post-exile experiences and identities; and to identify the government's role in creating conditions where the women were either able or unable to continue in their profession. In this study, I employed both a transnational feminist methodology and narrative inquiry. The analysis process included an emphasis on the storying stories model, poetic transcription, and concentric storying. The women’s voices are represented in various forms throughout the document including individual and collective narratives. Each narrative contributed to a detailed picture of immigration and settlement processes as women spoke of continuing their education, knowing or learning the official language, and contributing to Canadian society and the economy. The findings challenge the image of a victimized and submissive refugee woman, and bring to the centre of discourse the image of the refugee woman as a skilled professional who often remains un- or underemployed in her new country. The dissertation makes an important contribution to an underdeveloped area in the research literature, and has the potential to inform immigration, settlement, and teacher education policies and practices in Canada and elsewhere. iii
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".