Bibliographic record
Abstract
Most if not all social policies entail redistribution of scarce public resources from central government to regional and local authorities, to individual citizens or non-government agencies. Governments use a wide variety of instruments to allocate public funds, including direct state provision of subsidies and goods and services, setting budgets at different levels, and regulation of social insurance schemes. Most industrialised countries have developed budget models based on implicit or explicit allocation criteria. Governments usually start by determining global budgets for an entire category of public spending and then specifying the amounts allocated for categories of spending, and next, the budgets for individual agencies. Within such a 'cascading' model, the lower level budgets may be more controversial than the global budgets, as they directly affect the amounts available to individual actors in the system, e.g. hospitals or health insurance agencies. Setting budgets not only shifts decision-making authority but also financial risks from the central government to decentralised actors. The introduction of the prospective budgeting model for the Dutch sickness funds illustrates why determining budgets is not merely a matter of choosing objective allocation criteria, but also, of interaction between state and stakeholders. In the typical Dutch neocorporatist policy arena, where organised interests share responsibilities with government for the shaping and implementation of social policies, the health insurance agencies actively participated in the development of the budget model.
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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".