Special Schooling for Indigenous Students: a New Form of Racial Discrimination?
Bibliographic record
Abstract
Abstract Recent reports on Indigenous education have revealed that high proportions of students have been placed in special classes for intellectual disability or behaviour disorders. This is not an isolated phenomenon. Indigenous students in Canada and Romani children in Europe are also disproportionately represented in special schooling. This paper asks whether systemic racism, which fails to perceive cultural differences between the ethos of Australian educational systems and the experiences and abilities of Indigenous students, is the catalyst for placing many Indigenous students in special schooling, away from the mainstream. The paper applies an analysis based on anti-discrimination law to argue that while allocation on the basis of intellectual disability or behaviour disorders may not be deliberate racism, the criteria developed for the allocation may be measuring conformity to the dominant culture. If the policies underlying this segregation are unreasonable in the circumstances, they could constitute indirect racial discrimination against Indigenous students. Educational authorities could be liable in law, even though the effect on Indigenous students is unintentional and said to be for the students’ “own good”.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".