Bibliographic record
Abstract
Abstract This paper compares the social investment policy reforms that have been introduced by the two Anglo-Saxon liberal welfare regimes of Canada and Australia and the two East Asian welfare regimes of Japan and South Korea since the 1990s. The paper examines the causes of these social policy changes, and asks why these seemingly different contexts produce such similar policy idea. While all four countries share similar broad ideational template and language of social investment, they differ in terms of their target groups and policy instruments. Whereas Canada and Australia have focused their social investment policies on children through ECEC (what I call an “invest in the future” model); Japan and South Korea have approached social investment from a more general human capital and economic activation perspective (what I call a “human capital activation” model). As a result, social investment policies in these countries have targeted more broadly on children, women, and the elderly. I argue that these differences in social investment approaches stem from the differences in their social, political and economic contexts, and the political economic legacies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".