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Record W4410839617 · doi:10.1017/s0143814x25000078

Factors facilitating the adoption of wellbeing budgets in New Zealand: a case study with budget actors

2025· article· en· W4410839617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Public Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of SaskatchewanUniversity of Regina
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBusinessProcess managementPublic economicsEconomics

Abstract

fetched live from OpenAlex

Abstract New Zealand made international waves when it implemented a wellbeing budget in 2019. We investigated the factors which facilitated the adoption of this novel budgeting policy. In interviews with 22 key informants from New Zealand’s central government, most interviewees (90% and over) emphasized the impact of politics, internal direction, and the international policy environment as key factors of effect on the formulation and adoption of wellbeing budgeting. Results of our study add new insights to Good’s theory that predicts similar motivations and behaviors to be expected from groups of budget actors who inhabit monolithic roles of politicians, treasury officials, and ministerial bureaucrats. Rather, even with inherent tensions within budget actor groups, they can be positioned to debate differing approaches that lead to the aim of adopting innovative policy. Wellbeing budgetary reform may be undertaken with a combination of legislation, fostering public sector debate, and responding to global conditions of uncertainty.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.371
Teacher spread0.320 · 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