Women's organizations are different: their response to shifts in Canadian policy
Bibliographic record
Abstract
[Paragraph 1 of Introduction]: There are an estimated 200,000 nonprofit, nongovernmental organizations in Canada today offering a wide array of services to all segments of the population, ranging from food banks, women’s shelters, children’s aid societies, and immigrant service organizations to environmental protection agencies, opera companies and sporting societies (Browne, 1996). A significant, but unknown, percentage of voluntary organizations are led by women and governed by boards that are predominantly made up of women. Despite the 2 pervasiveness of these organizations, there has been little research focusing on them. We seek to redress this neglect by comparing 351 women’s voluntary organizations to 294 ‘other’ (gender neutral) voluntary organizations. Specifically, this paper investigates whether there are differences in attitudes, behaviours and perceptions between the leaders of women’s voluntary organizations and the leaders of ‘other’ voluntary organizations regarding: 1) perceptions of the environment; 2) outlook for the future; 3) perceptions of the impact of the external environment on the organization; 4) organizational changes made in response to environmental pressures; and 5) collaborative behaviour and attitudes. Keywords: CVSS, Centre for Voluntary Sector Studies, Working Paper Series,TRSM, Ted Rogers School of Management Citation:
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".