Bibliographic record
Abstract
In the remote schooling context, much recent media attention has been directed to issues of poor attendance, low attainment rates of minimal benchmarks in literacy and numeracy, poor retention and the virtual absence of transitions from school to work. The Australian government's recent ‘Gonski review’ (Review of Funding for Schooling – Final Report 2011) also strongly advocates the need to increase investment and effort into remote education across Australia in order to address the concerns of under-achievement, particularly of Indigenous students. Large-scale policies designed to improve access to services have caused a significant increase in services delivered from external sources, policy development at all levels of government, and tight accountability measures that affect remote communities and in turn, schools in various ways. Remote educators find themselves caught in the middle of this systemic discourse and the voices and values that exist in the remote communities where they live. Within this complex environment, the purpose of this article is to amplify Indigenous community voices and values in the discourse and by doing so, challenge ourselves as educators and educational leaders to examine the question: ‘While we're busy delivering education, is anybody learning anything?’ This article focuses on the Anangu (Pitjantjatjara/Yankunytjatjara) context of the North-West of South Australia, southern regions of the Northern Territory and into Western Australia. This region is referred to as the ‘tri-state’ region. Using a qualitative methodology, this article examines three Pitjantjatjara language oral narrative transcripts where Anangu reflect on their experiences of growing up and learning. By privileging these Anangu voices in the dialogue about learning in the remote Aboriginal community context, key themes are identified and analysed, highlighting important considerations for remote educators in understanding the values and cultural elements that inform Anangu students in their engagement with a formal education context.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.052 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".