Bibliographic record
Abstract
Abstract The creation of every social programme entails decisions about governance—about how these programmes are to be funded and administered. Policymakers have made varying choices about the territorial organization of social programme governance, as well as the mix of public and private actors involved in their financing, administration, and delivery. These decisions are highly consequential, shaping the relative power of different constituencies and governing bodies. Governance systems also reflect views about central versus local power, the role of religious and other groups in social provision, and the balance between markets versus states in providing for human welfare needs. This chapter examines social programme governance from a historical and a cross-national perspective to elucidate key patterns and trends. The first half of the chapter focuses on the public–private mix in welfare governance, while the second explores territorial governance, with a specific focus on federalism. One important theme in this chapter concerns the need to challenge assumptions that welfare states are monolithic, highly centralized, and state dominated. Instead, contemporary welfare regimes are mixed systems in which policy development and implementation often take place through non-state actors and/or at subnational levels of government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| 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 | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.016 |
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".