Religion as Mechanism of Adaptation for Immigrants: The Case of African Migrant Students in a South African Tertiary Institution
Bibliographic record
Abstract
While most scholars acknowledge the salience of migrants' transnational economic, political and socio-cultural practices, it is only recently that they have begun to pay attention to the relationship between religion and migration. Migration affects the entirety of a person's being because it involves an emotional crisis caused by the migrants' separation from their natural physical and social environment and a psychological problem of adjusting to the new one. Religious beliefs appear to be useful in serving to restore the inner balance of an individual and reducing the levels of anxiety amongst immigrants. Previous immigrant religion research has however focused mostly on immigrants in America with a recent focus on Canada, Australia, and Western Europe. This paper however focuses on a different context in Africa by examining the role of religion in the adaptation experiences of migrants from other African countries who have migrated to South Africa for study purposes. Using in-depth interviews, the paper examines the different ways in which religion influences their adaptation into the new context. The findings reveal that migration increases religiosity among migrants, facilitates aspects of immigrant adaptation such as academic achievement, emotional adjustment, negotiation of gender identity and building of social capital through social networks. Keywords: Religious Beliefs, Immigrant Adaptation, Social Networks, Gender Identity
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".