Adapting to a marketised system: Network analysis of a personalisation scheme in early implementation
Bibliographic record
Abstract
As governments worldwide turn to personalised budgets and market-based solutions for the distribution of care services, the care sector is challenged to adapt to new ways of working. The Australian National Disability Insurance Scheme (NDIS) is an example of a personalised funding scheme that began full implementation in July 2016. It is presented as providing greater choice and control for people with lifelong disability in Australia. It is argued that the changes to the disability care sector that result from the NDIS will have profound impacts for the care sector and also the quality of care and well-being of individuals with a disability. Once established, the NDIS will join similar schemes in the UK and Europe as one of the most extensive public service markets in the world in terms of numbers of clients, geographical spread, and potential for service innovation. This paper reports on a network analysis of service provider adaptation in two locations-providing early insight into the implementation challenges facing the NDIS and the reconstruction of the disability service market. It demonstrates that organisations are facing challenges in adapting to the new market context and seek advice about adaptation from a stratified set of sources.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".