Governing Health Care in an Imperfect World: Hierarchy, Markets and Networks in China and Thailand
Bibliographic record
Abstract
“Anything but the government” has been a popular sentiment in public policy circles for at least two decades. Initially, the sentiment favoured transitions from governments to market-based governance regimes but the tilt has shifted towards transition from governments to network governance in recent years (for discussion of the key relevant concepts, see Lowndes and Skelcher 1998). Much discussion on the subject suggests that such shifts from hierarchical to non-hierarchical governance are both unavoidable and desirable for addressing contemporary complex multi-actor problems which more traditional government-based arrangements find difficult to “steer” (Weber et al. 2011; Lange et al. 2013). Many proponents, for example, claim “network governance” or “collaborative governance” combines the best of both government-and market-based arrangements by bringing together key public and private actors in a policy sector in a constructive and inexpensive way (Rhodes 1997). This claim is no more than an article of faith, however, as there is little evidence supporting it and a lot of evidence contradicting this thesis (see Kj-r 2004; van Kersbergen and van Waarden 2004; Adger and Jordan 2009; Howlett et al. 2009, Hysing 2009). It is entirely possible that network governance combines and indeed compounds the ill-effects of both governments and markets rather than improves upon them and this is a subject area requiring further empirical examination. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".