Social Exclusion/Inclusion and Australian First Nations LGBTIQ+ Young People’s Wellbeing
Bibliographic record
Abstract
There is little known about the social, cultural and emotional wellbeing (SCEWB) of Aboriginal and Torres Strait Islander LGBTIQ+ young people in Australia. What research exists does not disaggregate young people’s experiences from those of their adult Aboriginal and Torres Strait Islander LGBTIQ+ peers. The research that forms the basis for this article is one of the first conducted in Australia on this topic. The article uses information from in-depth interviews to inform concepts of social inclusion and exclusion for this population group. The interviews demonstrate the different ways in which social inclusion/exclusion practices, patterns and process within First Nations communities and non-Indigenous LGBTIQ+ communities impact on the SCEWB of these young people. The research demonstrates the importance of acceptance and support from families in particular the centrality of mothers to young people feeling accepted, safe and able to successfully overcome challenges to SCEWB. Non-Indigenous urban LGBTIQ+ communities are at times seen as a “second family” for young people, however, structural racism within these communities is also seen as a problem for young people’s inclusion. This article contributes significant new evidence on the impact of inclusion/exclusion on the SCEWB of Australian First Nations LGBTIQ+ youth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.006 |
| 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".