Bibliographic record
Abstract
The adoption of high level strategic planning is gaining in popularity as the new strategic role for governments post New Public Management. Documents with state level goals supported to varying extents by strategies, actions and targets are apparent in jurisdictions in Australia, Canada and Scandinavia. Yet research is questioning whether or not politicians will take on the leadership role expected of them in this new process (see for example the recent work of Tilli on the Finnish experience). This paper will examine the adoption of state strategic planning in Australia and analyse views of political and public sector actors in executive government in Western Australia on their perceptions of these plans as a tool in public administration. Perceptions were gathered during interviews as part of broader doctoral research into coordination strategies introduced by the Gallop government in the period 2001 to 2005. While the concept is gaining popularity around the nation, it is too early yet to determine whether or not this is a passing fad or a new direction in public administration. It is argued that, in Western Australia at least, politicians and their advisors are more motivated to be “strategic by stealth, ” maintaining a cautious approach to what they put into the public domain and can therefore be held to account for. Beyond-election targets do exist in specific policy areas but they are not systematically compiled nor are they developed in any whole of government sense.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.615 | 0.415 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".