Bibliographic record
Abstract
Young people ‘ageing out of care’ have to manage multiple transitions – leaving ‘home’, moving into independent accommodation, leaving school and trying to find work or some other means of support, becoming financially independent, and often becoming parents - at a much younger age and with fewer resources and supports than other young people their age. This paper presents the findings of the fourth interview in the follow-up to the Longitudinal Study of Wards Leaving Care study in New South Wales, and focuses on three main questions. How were these young people faring 4–5 years after leaving care compared with other young people their age? How were they faring compared with their circumstances and outcomes 12 months after leaving care? What predicted better outcomes and not-so-good outcomes? While the pattern of low levels of educational attainment, and high rates of unemployment, mobility, homelessness, financial difficulty, loneliness and physical and mental health problems was consistent with that from other research in England, Ireland, Canada and the United States, some young people were faring quite well and much better than others. Understanding why is important in trying to support young people leaving care. The paper highlights some of the implications for policy and practice.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".