Factors facilitating the adoption of wellbeing budgets in New Zealand: a case study with budget actors
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".