The impact(s) of South Sudanese cultural marriage practices on women's pursuit of post-secondary education in Canada
Bibliographic record
Abstract
The central question asked in this thesis is "How/does South Sudanese cultural marriage practices impact South Sudanese women's pursuit of post-secondary education in Canada?" This thesis, therefore, aims to investigate how cultural marriage practices impact South Sudanese women in their quest for post-secondary education. This study focuses on the experiences of six South Sudanese women who immigrated to Canada as refugees. The focus is drawn toward their educational aspirations and their cultural experiences. To provide context for this research, literature on migration, the making of a refugee, the resettlement process, assimilation, refugee experiences in host societies, and refugee women's experiences are broadly discussed. The research findings explain the effects of South Sudanese cultural marriage practices on South Sudanese women who have immigrated to Canada as refugees. Using African Feminism as a theoretical perspective, the study explores the importance of centring African women's voices and experiences with the aim of creating gender equality. Data collection methods incorporated in this research are semi-structured interviewing and autoethnography, as they provide an in-depth analysis of the study. The study results show how South Sudanese cultural marriage expectations can coexist with women's desire to pursue post-secondary education.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".