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Record W4301081488 · doi:10.46692/9781447310280.010

Policy and policy analysis in Australian states

2015· other· en· W4301081488 on OpenAlexaboutno aff
John Phillimore, Tracey Arklay

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsPolicy analysisPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

Introduction Understanding policy analysis within state and territory governments (state governments hereafter) in Australia presents quite a challenge. State governments are part of a federation in which the Commonwealth government is fiscally dominant and has greatly expanded its policy ambition and reach over the past century. As a result, many areas of government activity that were previously the sole preserve of state governments have become concurrent areas of policy formulation and implementation (Fenna, 2012). This involves bilateral and multilateral negotiations and relations between state and Commonwealth leaders, ministers and officials. Determining the precise role of states individually or collectively in such shared policy areas is inherently difficult. Relatedly, intergovernmental relations (IGR) are ‘notoriously opaque’ (Painter, 1998, p 71) and generally resistant to study, and, as a consequence, they have been neglected relative to the fiscal and judicial dimensions of federal systems. This is particularly true of Australian federalism. There have been very few detailed studies of the inner workings of intergovernmental institutions such as ministerial councils (one exception is Jones, 2008) or even the better-known peak bodies such as the Council of Australian Governments (COAG). Separately, there has unfortunately been little analysis or even description of policy processes within state governments over the past 15 years, beyond the comparative work of Galligan (1986, 1988), Birrell (1987), Painter (1987), Peachment (1995) and Spoehr and Broomhill (1995) and state-specific studies (eg Davis, 1995; Costar and Economou, 1999; Spoehr, 1999, 2005, 2009, 2013; Crowley, 2012). Australia is not unique in this relative lack of focus on sub-national governments (on Canada, see, eg, McArthur, 2007, pp 238–9). This absence of attention to state-level policy can be explained by a combination of factors. First, many of the trends at sub-national level – in particular, the introduction of New Public Management (NPM), the rise of central agencies (Halligan and Power, 1992) and the turn towards market approaches – have also occurred at the national level, which has tended to receive most attention. Second, the centralisation trend in Australian federalism has naturally led to a focus on the federal government, a fact made more understandable in light of the introduction of competition and regulatory harmonisation processes that have served to reduce the differences in government policy and practices between the states.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.087
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0040.007
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.024
GPT teacher head0.371
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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