Examining remote Australian First Nations boarding through capital theory lenses
Bibliographic record
Abstract
In Australia, boarding schools and residential facilities for remote Aboriginal and Torres Strait Islander (First Nations) students have long been part of the educational landscape. Policy settings are paying considerable attention to boarding schools and residential colleges as secondary schooling options for First Nations students, particularly for those from remote areas. Further, First Nations education is seeing increased investment in scholarship programmes, transition support services and establishment of national boarding standards.There is an emerging body of qualitative evidence about the experiences and outcomes of boarding for remote First Nations students. However, in Australia there are no publicly available evaluations showing quantitative impacts of boarding.In this paper, the authors critically examine boarding using three capital theory lenses: social/cultural capital (based on Bourdieu), human capital (based on Becker), and identity capital (based on Erikson). Using these lenses we intend to go beyond an understanding of impact on individuals towards a more nuanced consideration of the social, cultural, health and well-being consequences of pursuing boarding as strategic policy for First Nations students in Australia.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".