State-Funded Feminism: A Methodology for the History of Public Funding for Canada’s Voluntary Sector
Bibliographic record
Abstract
Evolving digital tools are opening new avenues of inquiry for historical research. This research note provides a methodology for collecting historical data on government grants to non-governmental organizations. State funding has had a profound impact on the voluntary sector since the 1960s. The women’s movement, in particular, has been deeply impacted by the state’s intervention into the voluntary sector. Using grants data has the potential to provide unique insights into the dynamics between the state and civil society. Among other things, it enables historians to track organizations over time periods and jurisdictions; to document the diversity of organizations in the voluntary sector; to identify new organizations for historical research; and to compare funding trends among organizations or governments by region, time period, and issue area. The methodology presented in this study includes the use of digital tools to collect, process, and analyze grants data from multiple levels of government while sharing some preliminary findings on the impact of state funding on the women’s movement in Canada since the 1960s. The data reveal, among other things, how state funding has fostered the proliferation of voluntary organizations and privileged certain organizations within the movement as well as vast disparities in funding among regions within Canada.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.032 |
| Science and technology studies | 0.028 | 0.036 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".