The Danger of being a Young Female Migrant: A Case Study Female Refugees in Musina Town in South Africa
Bibliographic record
Abstract
Several studies show that international migrants across the globe extremely face challenges upon their arrival in the host countries. This constant influx of international population movement is driven by factors such as escaping from poverty, seeking better livelihoods, or escaping from political upheavals and civil strife, such as wars. There have been several studies in South Africa that generally explored challenges faced by the international migrant youth but not necessarily on the gendered nature of migration. This study argues that migration affects males and females inversely. Thus this study aimed to contextually explore the danger of being a young female migrant by following a qualitative research approach using female refugees in Musina town as a case study. Nine participants were purposively and conveniently selected and semi-structured face-to-face interviews with open-ended questions were followed to collect data that is analysed thematically in this paper. The Nvivo software was used to manage and organise data. Findings reveal that young female migrants face challenges from the cross-bordering where they are at risk of being raped. Findings further show that upon their arrival in South Africa, female young migrants face challenges such as exclusion from basic health care services due to lack of immigration documents, sex work, and exploitation by local citizens as well as victimization by the police. The security at border posts should thus be tightened and the defence forces should jointly work with the police officials to deport female migrant youth who migrate illegally and stakeholders in South Africa should run educational programmes where the illegal immigrants would be educated about the risks of cross-boarding to South Africa without legal immigration permits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".