Punching above their weight or falling flat? Flagship policy modernisation initiatives in Australia, Britain, Canada, and New Zealand
Bibliographic record
Abstract
Abstract Concerns of a decline in public service policy capacity coupled with evolving policy advisory systems have seen public services seek to reform their policy capacity and practices. This article examines the flagship policy modernisation initiatives launched by the Australian, Canadian, British, and New Zealand governments. Comparative analysis reveals a shared emphasis on overarching objectives, but important differences in their design, how they are embedded within the public service, and their comprehensiveness. The New Zealand and British initiatives are found to be the most comprehensive and stable, while the Canadian and Australian approaches suffer from repeated reinvention exercises and resource and leadership precarity. An examination of these initiatives also provides new insights into understanding the trade‐offs and tensions around how these initiatives aim to address public service policymaking and effective advisory system participation. Points for practitioners The public service's role within advisory systems is evolving and needs to be carefully reconsidered. Senior officials need to get serious about effectively scoping reform initiatives and being clearer about the trade‐offs associated with broad or more targeted approaches. Initiatives are drastically under‐resourced even in the best of scenarios. Governments and senior officials need to step up and invest in sustainable and well‐institutionalised initiatives. Practitioners will need to be creative about how and where they can access tools and approaches to improve their policymaking in cases where governments continue to under‐resource policy modernisation.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".