Yarning as a method for building sexual wellbeing among urban Aboriginal young people in Australia
Bibliographic record
Abstract
This paper describes the strategies used by Aboriginal young people to build positive relationships and sexual wellbeing. It does so to counter the risk-focussed narratives present in much existing research and to showcase the resourcefulness of Aboriginal young people. We used peer-interview methods to collect qualitative data from 52 Aboriginal young people living in western Sydney, Australia. Participants reported a strong desire to stay safe and healthy in their sexual relationships and to achieve this they relied heavily on oral communication and yarning strategies. Participants viewed communication as a way to gain or give advice (about bodies, infections, pregnancy, relationships); to assess the acceptability and safety of potential partners; to negotiate consent with partners; to build positive relationships; and to get themselves out of unhealthy relationships. Participants also discussed ‘self-talk’ as a strategy for building sexual wellbeing, referring to narratives of self-respect and pride in culture as important in establishing Aboriginal young people’s positive views of self and as deserving of respectful and safe sexual relationships. These findings suggest that future programmes and interventions based on yarning could be well-regarded, given it is a cultural form of pedagogy and a strategy Aboriginal young people already use to build positive relationships and identities.
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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 | 0.007 | 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 | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".