The Adaptation of South Sudanese Christian Refugees in Ottawa, Canada: Social Capital, Segmented Assimilation and Religious Organization
Bibliographic record
Abstract
This dissertation examines the adaptation of Christian refugees from Southern Sudan—primarily Dinkas and mostly educated—to living in Ottawa, Canada, not historically a gateway immigrant city. The discussion is based on sustained observation, documentation and analysis of South Sudanese refugees between 2005 and 2009, including 32 recorded interviews of adults, as well as a focus group held with young adults. It examines the findings through the lenses of social capital, with its focus on trust and reciprocity, and segmented assimilation to study the South Sudanese refugees’ integration through their most important groupings: ethnic, gendered, racial and religious. The study also focuses on the cultural, gender and language dynamics of a nascent South Sudanese-focused congregation and a related East African congregation. The experiences of Anglican and Catholic congregations with Christian Sudanese refugees were also examined. The research suggests that inter-culturally competent ethnic and religious leadership is central to the ability of migrant groups in the Global North to have enough bonding social capital to mediate the adaptation process and to bridge or link to other groups. First-wave, mostly male, educated refugees often have the inter-cultural skills and agency to set up effective organizations, but a continued focus on their region of origin, facilitated by the Internet and cell phones, makes a sustained emphasis on organizational-supported living in Canada difficult. While the values of many Sudanese-born women and their children converge with those of mainstream Canadian society, men living within patriarchal value systems, supported by literal interpretations of Holy Scriptures, face challenges, and the resulting conflicts threaten family cohesion. Both the denominational and the ethnic churches, in supporting new migrants spiritually and socially, are caught between denominational parameters and goals of ethnic identity, culture and values maintenance, made more difficult by the Sudanese not having a common language. The dissertation also begins to analyze the impact for recent African Christian immigrants of a culture that emphasizes individual rights, including the effects of the increasing presence of openly gay leaders in the Canadian but not in the African Church.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 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) | 0.002 | 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".