Applying SDAC 2009 to the OECD Integration Scenario for Disability Employment
Bibliographic record
Abstract
The paper analyses Confidentialised Unit Record Files (CURF) data from the 2009 Australian Bureau of Statistics (ABS) Survey of Ageing, Disability and Carers (SDAC) to estimate the proportion of Australians with disability and an employment restriction who are not in the labour force and who want to work. This result was 24–26 per cent depending on the treatment of various survey responses. This represents a pool of 200,000 persons not in the labour force with disability who state they can work. This work intention rate is higher than the EU average overall, but tends to be lower than the EU average for older men and higher for younger women with disability outside of the labour market. The OECD integration model for persons with disability has relied upon the EU average work intention rate. This analysis applies the SDAC work intention rate to the OECD model. The SDAC data combined with updated population and labour force projections validate the previous conclusion of the OECD model: that in Australia allowing people with disability who want to work to enter find employment would reduce the fiscal gap caused by the ageing of the population by roughly a quarter.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".