Bibliographic record
Abstract
Abstract Using the Australian Bureau of Statistics 1998 Survey of Disability, Ageing and Carers, this study examines the effects of disability on four labour market outcomes: not in the labour force, unemployed, part‐time employed and full‐time employed. The detailed information on health available in the dataset also facilitates investigation of the dependence of effects on the characteristics of the disability, including severity, impairment type and age of onset. Disability is found to have substantial effects on labour force status, on average acting to decrease the probability of labour force participation by one‐quarter for males and one‐fifth for females. For males, the decrease in fulltime employment accounts for almost all of the decrease in labour force participation associated with disability; for females, disability has negative effects on both full‐time and part‐time employment. Analysis of disability characteristics shows that adverse effects on labour force status are increasing in the severity of the disability and are also worse for those with more than one type of impairment and for those who experience disability onset at older ages. There is evidence that the adverse effects of disability are lower for males who completed their education after the onset of the disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".