Researching resilience: lessons learned from working with rural, Sesotho-speaking South African young people
Bibliographic record
Abstract
Theories of youth resilience neglect youths’ lived experiences of what facilitates positive adjustment to hardship. The Pathways-to-Resilience Study addressed this by inviting Canadian, Chinese, Colombian, New Zealand and South African (SA) youths to share their resilience-related knowledge. In this article I report the challenges endemic to the rural, resource-poor, South African research site that complicated this Pathways ideal. I illustrate that blind application of a multi-country study design, albeit well-designed, potentially excludes youths with inaccessible parents, high mobility, and/or cellular telephone contact details. Additionally, I show that one-on-one interview methods do not serve Sesotho-speaking youths well, and that the inclusion of adult ‘insiders’ in a research team does not guarantee regard for local youths’ insights. I comment critically on how these challenges were addressed and use this to propose seven lessons that are likely to inform, and support, youth-advantaging qualitative research in similar majority-world contexts.
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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 | 0.058 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| 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".