Bibliographic record
Abstract
Abstract In discourse around disability there has been a shift away from a ‘medical model’, which perceives disability as an individual problem to be ‘cured’ or contained, towards a ‘social model’. The latter focuses on the relationship between people with disabilities and their social environment, locating the required interventions within the realm of social policy and institutional practice. Drawing upon a small qualitative study conducted in Melbourne, this article argues that recent plans by the Australian government to introduce mutual obligation requirements for recipients of the Disability Support Pension (DSP) sit in tension with this shift from the medical to the social models of disability. Mutual obligation is based on the assumption that income support recipients need to be taught how to be more ‘self‐reliant’, to ‘participate’ in society more fully and to become ‘active’, rather than ‘passive’, citizens. This language appears to overlap with that used to articulate a social model, which places emphasis on participation in the community and attempts a shift away from reliance on the medical profession. However, examples from interviews conducted with current and former DSP recipients demonstrate that, in practice, mutual obligation is likely to reinforce a medical model of disability, frame DSP recipients as ‘conditional’ citizens and ignore the obligations of the state and society regarding access and inclusiveness for people with disabilities.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".