Policy Design, Instrument Choice, and Policy Feedback Effects of Institutional Business Power: Varieties of Child Care Investments in Australia, Canada, and Germany
Bibliographic record
Abstract
ABSTRACT Recent research examining the policy feedback effects of delegated governance note that private actors accumulate institutional business power—distinct from structural and instrumental power typical of businesses—through such mechanisms as delegation, deregulation, and accretion. Business power, once granted by the government, accumulates and institutionalizes over time, even when the state's policy goals may change. In this article, we apply these insights but offer a modified theoretical model that acknowledges the interrelationship of these mechanisms and examines their interaction over time. We draw on the case of early childhood education and care (ECEC) policy in three federal systems—Australia, Canada, and Germany. We develop an original index that maps policy making across the three sub‐national contexts across time to examine the impact of institutionalized private actors of varying status (for‐profit, not‐for‐profit) and power. We argue that regulatory frameworks introduced at the original delegation stage at t1 affect the likelihood of accretion and demonstrate the inter‐relationship between institutional business power and regulatory and governance factors which shape the early years market. Importantly, these accretion dynamics, characterized by increasing market share of for‐profit providers, has the capacity to significantly undermine the social investment logics underpinning government action by constraining access to ECEC services (through high costs), reducing regulations, and thus undermining the quality of ECEC services for parents and children.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".