Red Dirt Thinking on Educational Disadvantage
Bibliographic record
Abstract
When people talk about education of remote Aboriginal and Torres Strait Islander students, the language used is often replete with messages of failure and deficit, of disparity and problems. This language is reflected in statistics that on the surface seem unambiguous in their demonstration of poor outcomes for remote Aboriginal and Torres Strait Islander students. A range of data support this view, including the National Action Plan—Literacy and Numeracy (NAPLAN) achievement data, school attendance data, Australian Bureau of Statistics Census data and other compilations such as the Productivity Commission's biennial Overcoming Indigenous Disadvantage report. These data, briefly summarised in this article, paint a bleak picture of the state of education in remote Australia and are at least in part responsible for a number of government initiatives (state, territory and Commonwealth) designed to ‘close the gap’. For all the programs, policies and initiatives designed to address disadvantage, the results seem to suggest that the progress, as measured in the data, is too slow to make any significant difference to the apparent difference between remote Aboriginal and Torres Strait Islander schools and those in the broader community. We are left with a discourse that is replete with illustrations of poor outcomes and failures and does little to acknowledge the richness, diversity and achievement of those living in remote Australia. The purpose of this article is to challenge the ideas of ‘disadvantage’ and ‘advantage’ as they are constructed in policy and consequently reported in data. It proposes alternative ways of thinking about remote educational disadvantage, based on a reading of relevant literature and the early observations of the Cooperative Research Centre for Remote Economic Participation's Remote Education Systems project. It is a formative work, designed to promote and frame a deeper discussion with remote education stakeholders. It asks how relative advantage might be defined if the ontologies, axiologies, epistemologies and cosmologies of remote Aboriginal and Torres Strait Islander families were more fully taken into account in the education system's discourse within/of remote schooling. Based on what we have termed ‘red dirt thinking’ it goes on to ask if and what alternative measures of success could be applied in remote contexts where ways of knowing, being, doing, believing and valuing often differ considerably from what the educational system imposes.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".