The Australian Public Service and Policy Advising: Meeting the Challenges of 21st Century Governance
Bibliographic record
Abstract
This concluding article summarises the case study findings comprising the Special Issue on ‘Advising Australian Federal Governments: Assessing the Evolving Capacity and Role of the Australian Public Service’, identifies and discusses cross-cutting issues, and considers strategic implications for future practice and research. It reviews key findings from six case studies – Treasury, Prime Minister and Cabinet, Intergovernmental Relations, Housing, the BER Stimulus program, and Defence – and assesses the policy advising capacity of the Australian Public Service, with a focus on the policy-political interface between governments and officials. Putting recent experience in historical context, it considers the performance of the Commonwealth's policy advisory system, the impact of prime ministers and centralisation, the link between advising and analytic capacities, the system's resilience and readiness, whether recent dissatisfaction over APS advising reflect lack of capacity or a culture clash, and the responsibility for ensuring high-quality policy advice. It recommends developing a more systematic approach to assessing policy advising capability, building on recent APS reforms.
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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| 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".