Bibliographic record
Abstract
It has taken almost 15 years for an Australian government to again proceed with major financial reform. Earlier this year, the then minister for finance when introducing new legislation implementing the reforms under one Act (the Public Governance, Performance and Accountability Act 2013) noted that the system was ‘not broken’ but that it ‘creaks at times’. That Act represents about one half of the proposed reforms but is central to their success. One of the more interesting aspects of the reforms is that they were developed in an open process with the involvement of both public and private sector advisers over a three-year period and oversight by the Department of Finance, similar to the approach taken with the ground-breaking public service reforms in Australia in the 1990s. The focus is largely on financial reform as part of good governance, stressing performance and accountability, but also giving prominence to risk management and the associated notion of ‘earned autonomy’ and less ‘red tape’. In addition, attention is given to the need to establish a governance framework that recognizes the increasing co-operation and collaboration across agencies and entities, across governments at all levels and across sectors of the economy. We continue to learn from research in both the UK and Canada in particular, recognizing both the similarities and differences between the public and private sectors.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".