Supported Decision-Making for People with Cognitive Impairments: An Australian Perspective?
Bibliographic record
Abstract
Honouring the requirement of the Convention on the Rights of Persons with Disabilities to introduce supported decision-making poses many challenges. Not least of those challenges is in writing laws and devising policies which facilitate access to formal and informal supports for large numbers of citizens requiring assistance with day-to-day issues such as dealing with welfare agencies, managing income security payments, or making health care decisions. Old measures such as representative payee schemes or “nominee” arrangements are not compatible with the CRPD. However, as comparatively routine social security or other government services become increasingly complex to navigate, and as self-managed or personalised budgets better recognise self-agency, any “off the shelf” measures become more difficult to craft and difficult to resource. This paper focuses on recent endeavours of the Australian Law Reform Commission and other local and overseas law reform and policy initiatives to tackle challenges posed both for ordinary citizens and those covered by special programs (such as Australia’s National Disability Insurance Scheme and “disability trusts” in Australia and Canada).
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".