Remote secondary education retention: What helps First Nations students stay until, and complete, year 12
Bibliographic record
Abstract
Over recent years there has been a strong emphasis on year 12 completion as an indicator of success in remote First Nations education. The research reported in this article explores what students, school staff and community members say leads to secondary school retention and, ultimately, completion. The research was conducted in the Northern Territory and Western Australia during 2023 by a team of researchers from Batchelor Institute of Indigenous Tertiary Education, Curtin University and University of Notre Dame. The research focused on remote and very remote independent and Catholic schools. The findings suggest several factors encourage retention, including the supportive role of families (although questions remain on how families might also be supported to encourage their children), the quality of teachers and their teaching, specific school programs and initiatives, post-school pathways, and boarding schools. Student aspirations and motivation are also critical for retention. The findings have implications for schools, school systems, state and federal policies, and associated funding priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".