The implementation and impact of National's welfare conditionality in an international context
Bibliographic record
Abstract
This article considers the welfare policy of New Zealand's National-led government (2008-2017) within the context of international welfare conditionality. I propose that reforms undertaken during the National-led government's nine years in office align with a recent 'conditionality turn' that has taken place in high-income Anglophone countries. The article situates National's welfare reforms within similar changes undertaken by governments in the United Kingdom, United States, Australia, Canada and Ireland. As in New Zealand, welfare reform in these countries has emphasised behavioural conditionality targeting lone mothers, the sick and young people. Considering the reforms within the context of international welfare conditionality emphasises the endurance of ideas underpinning the reforms. The article argues that welfare conditionality has become embedded in the New Zealand welfare system and is shaping the policy of the Labour-led government elected in 2007.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".