Family, friendship, and strength among LGBTQ+ migrants in Cape Town, South Africa: A qualitative understanding
Bibliographic record
Abstract
The purpose of this qualitative study was to explore how migrants in South Africa identifying as lesbian, gay, bisexual, transgender, queer, or with other diverse sexual orientations or gender identities (LGBTQ+) describe and understand their pre-migration family experiences and how family and other social relationships facilitated strength during post-migration. We conducted six focus groups, consisting of both morning and afternoon sessions, which included a total of 30 LGBTQ+ migrants (ages 21–42). The following themes were identified using grounded theory: managing family responses during pre-migration: concealing, avoiding, disclosing; the power of (even) one: support during post-migration; “love is a very big thing”: drawing strength from chosen family; and “pulling myself up”: drawing strength from self-reliance. Findings demonstrate that many participants reported experiencing negative responses from family, but some continued to rely on family support after arriving in South Africa. Further, participants often depended on newfound friendships for support as well as their own internal resources. This self-reliance was facilitated in part by participants’ understanding that they could not depend on their families or other people because of the negative responses faced in their countries of origin. Implications for theory, research, and practice are discussed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| 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".