Do Subnational Governments Fund Organizations in Neoliberal Times? The Role of Critical Events in Provincial Funding of Women’s Organizations
Bibliographic record
Abstract
Countries around the world have adopted neoliberal or austerity policies. Among states that fund organizations, this may have a detrimental effect on civil society. Looking at the Canadian context, this article examines whether subnational governments, provinces, step in during times of national budget cuts and changing political environments to fund organizations. We do this by analyzing the effect of critical events, regime changes, and the founding of key organizations on state funding in the province of Nova Scotia between 1960 and 2014. We do this to examine how the interaction of national and subnational political context shapes subnational funding of organizations. We find that critical events appear to be linked to increases in provincial funding, however, do not appear to be linked to cuts in funding. Regime changes and founding of key organizations have less clear-cut relationships with provincial funding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".