Institutional Entrepreneurs and the Politics of Redesigning the Welfare State: The Case of Health Care
Bibliographic record
Abstract
Institutional entrepreneurs reconfigure control of different bases of power, across the public and private sectors. They are thus to be distinguished from policy innovators within the public sector and from commercial entrepreneurs within the private sector. Market-oriented policies in Britain, the Netherlands and the US, by attempting to introduce and/or harness market forces in combination with public funding and regulation, disrupted established relationships and established new footholds for multiple actors to undertake entrepreneurial activity spanning the public and private sectors. The very fact of large-scale policy change, moreover, created political and technical uncertainty around the future course of reforms. Hence the classical conditions for entrepreneurialism – heterogeneity and uncertainty – were put in place. Differences in the scale and pace of reform made for differences in the degree of heterogeneity and in the timing and duration of periods of uncertainty. Institutional entrepreneurs emerged from different bases, behaved in different ways and had different impacts in the three countries. Entrepreneurialism emerged principally within the ranks of providers in England, insurers in the Netherlands and state actors in the US. The bases from which these entrepreneurs emerged were determined initially by the nature of the policies adopted during moments of major policy change. In that sense, policy shaped entrepreneurial behaviour. But once institutional entrepreneurs had seized the opportunities created by the new policy frameworks, their behaviour shaped the ensuing course of policy development, and in various ways laid the groundwork for the next major episode of change. That behaviour was constrained by the existing political economy of health care. But those institutional entrepreneurs who succeeded (and not all did) expanded their power bases, even as the arrangements they created channeled the exercise of that power in new ways.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.067 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".