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Record W2506595216 · doi:10.1057/9780230228771_6

New Zealand: The Expansion of the State in a Liberal Welfare Regime

2008· book-chapter· en· W2506595216 on OpenAlexaff
Toni Ashton, Susan St John

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCommonwealthWelfare stateGovernment (linguistics)WelfarePublic policyPolitical scienceState (computer science)Social policyCoalition governmentConvergence (economics)Social WelfarePublic administrationEconomicsPublic economicsPolitical economyEconomic growthPoliticsLaw

Abstract

fetched live from OpenAlex

Internationally, New Zealand and Australia are often coupled together in some kind of an Antipodean version of the British welfare system. Yet, although they may be placed within the same “liberal welfare regime” camp, and both countries share a commonwealth heritage that has informed the development of their welfare states, there are significant differences in policy objectives, policy design, and institutional structures (St John 2004). In contrast to Australia’s federal system of government, New Zealand has a unicameral government that, until 1993, was elected via a first-past-the-post electoral system. 1 This system of government proved conducive to extreme policy swings and allowed some unusual experiments to be undertaken in both economic and social policy, including attempts to use private mechanisms to achieve public objectives. Although there is now some evidence of a trend toward convergence in some components of social policy across the two countries (McClelland and St John 2006), New Zealand remains unique and worthy of examination in its own right. Accordingly this chapter focuses primarily on New Zealand in the belief that its particular experience of the public-private dichotomy may be of interest to other countries. Some comparisons are drawn with health and pensions policies in Australia to illustrate the rather different approaches to social policy that have been taken in these neighboring countries. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.242
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.012
Scholarly communication0.0040.004
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.023
GPT teacher head0.265
Teacher spread0.242 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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