Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Australia, state, territory and commonwealth governments have established an array of workers' compensation systems that collectively seek to achieve the greatest return to work outcomes at the lowest cost to society. While sharing this important public health objective, these systems differ substantially in approach. There is much variance between the schemes with respect to policy and practice and very little quality published evidence regarding the relative impact of policy settings on return to work outcomes. <strong>The COMpensation Policy And Return to work Effectiveness (COMPARE) </strong>project was established to develop an evidence base that can support development and implementation of effective return to work policy in Australia. The project adopts a comparative effectiveness methodology, comparing outcomes between jurisdictions and using sophisticated statistical techniques to identify policy settings that have positive, negative or neutral effects on return to work and duration of income replacement. We also compare outcomes before and after changes in policy, such as amendments to workers' compensation legislation. The project team, led by Alex Collie, is supported by a national policy and data advisory group providing expert assistance, advice and guidance to the study investigators. The project is one part of a larger international study including Canadian and European workers' compensation jurisdictions. The project involves analysis of the National Dataset of Compensated Based Statistics and the National Return to Work Survey. The COMPARE project started in 2015 and has produced many findings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 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 it