Mental illnesses are not an ‘ideal type’ of disability for disability income support: Perceptions of policymakers in Australia and Canada
Bibliographic record
Abstract
Aim: This article aims to explore how policymakers conceptualise a person suitable for disability income support (DIS) and how this compares across two settings – Australia and Canada. Methods: A constructivist grounded theory approach was used; 45 policymakers in Australia and Canada were interviewed between March 2012 and September 2013. All policymakers are or were influential in the design or assessment of DIS. Results: Results found that the policymakers in both jurisdictions define a suitable person as having as an ‘ideal type’ of disability with five features – visibility, diagnostic proof, permanency, recognition as a medical illness and perceived as externally caused. Many of the policymakers described how mental illnesses are not an ‘ideal type’ of disability for DIS by juxtaposing the features of mental illnesses against physical illnesses. As such, mental illnesses were labelled imperfect disabilities and physical illnesses as ‘ideal type’ for DIS. Conclusions: The rise of DIS recipients has divided the once protected ‘deserving’ category of the disabled into more (‘ideal type’ of disability) and less deserving (imperfect disability). Such conceptualisations are important because these categories can influence the allocation of welfare resources.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".