Submission to Disability Royal Commission - First Nations People and the NDIS
Bibliographic record
Abstract
First Nations people are the most informed culturally and have the greatest capacity to engage with First Nations NDIS participants. But in the categories of skills recognised by the NDIS First Nations people’s cultural and communication capacity is not recognised, so they are often excluded from providing information on assessments and identifying support needs. Moreover, the categories of people (professions) who are recognised by the NDIS and enabled to speak about First Nations people often do not have these skills or knowledge. Their professional training and experience generally does not equip them to understand important cultural factors or communication issues important in working with First Nations people with disabilities. Thus we make the proposition that ‘those that know mostly can’t speak, those that can speak mostly don’t know’, in relation to important cultural and communication information on First Nations NDIS participants. This document describes the context of this proposition based on our experience in working as a bicultural team with First Nations NDIS participants. This document was first developed as a training resource for Northern Territory Public Guardians and is part of training provided to NDIS service providers. For more information contact damien@phoenixconsulting.com.au
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.146 | 0.049 |
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".