Measuring Change in Welfare State – Non-profit Relationships: Towards a Conceptual Framework
Bibliographic record
Abstract
Few systematic conceptual tools that can be applied across time and space have been used to measure state involvement with non-profits. Fewer examine the latter’s “place”, not only vis-à-vis the state, but in the overall political economy. Current examinations concentrate heavily on measuring levels of state and other sources of funding and levels of volunteerism. The dynamism, inherent in real state – non-profit relationships, has been lost in academic translation and analysis. The conceptual framework presented here suggests a bi-directional rather than linear analysis, and bridges theoretical work from the welfare state and health geography literatures. This framework is then applied to the case of Ontario’s community support services sector. First, current state administrative, regulatory and funding policies are analysed for the ways in which they affect non-profits ’ roles, identities, autonomy, and viability. First, Pierson’s (1994) programmatic retrenchment and Atkinson and Coleman’s (1989) and Coleman and Skogstad’s (1990) concepts of state coordinating capacity and autonomy are used to investigate the following questions regarding state policy change in
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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 | 0.060 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".