Concentrating or Sprinkling? Federal Funding for Indigenous, Women’s, and Environmental NGOs in Canada, 1972-2014
Bibliographic record
Abstract
Governments have a long history of funding nongovernmental organizations (NGOs) and their programs. While there is much work describing this funding, there has been little research systematically examining how the political and economic context shapes the level and type of funding NGOs receive. This article examines the factors that predict levels of government funding for NGOs over time, focusing on spending for interest groups in three areas, Indigenous, women, and the environment. We use data collected from the Canadian Public Accounts, which lists all grants to groups by the federal government from 1972 until 2014. We use these data to assess how federal funding has changed over this period, how funding across issue areas is related, and the role of political and economic factors in shaping rising and declining funding over time. We find that the factors that predict funding vary across issue areas. Our analysis also shows government’s tendency to sprinkle funding across a larger number of groups or concentrate it in a smaller number of organizations is strongly related to the party in power and the issue area.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".