Reacting to the Rise in Accountability Requirements: A Case Study of Status of Women Canada’s Women’s Program
Bibliographic record
Abstract
Nonprofit organizations working towards social progress on gender-based violence cannot effect systemic change alone; they depend on partnerships, networks, and funding to carry out their work.Many receive and even depend on government funding and support to move their missions forward.This work is a case study of the funds distributed for gender-based violence work through the Women's Program funding stream, administered by Status of Women Canada, between 1995 and 2015; it explores the intent of this funding, how the program evolved over the study period particularly with respect to rising accountability requirements, and the characteristics of and impacts on recipient organizations.This is a mixed methods research project, which includes a textual analysis of government-published documents pertaining to the Women's Program over the study period, the production of network maps of funded organizations, a quantitative analysis of distributed funds, and semi-structured interviews with both funded organizations and Status of Women Canada bureaucrats.Over the study period, there was a rise in accountability measures required of funded organizations.At the same time, funding recipients shifted away from smaller nonprofit organizations and towards larger and/or for-profit ones, as well as away from research and advocacy targets and towards service delivery.This exposes certain organizations -that is, low-revenue nonprofit organizations and networks that are not registered as charities and that are focused primarily on gender-based work -as being particularly vulnerable due to their dependence on government funding.Funding recipients consistently reported, however, that despite the issues in applying for, reporting I am deeply grateful to my supervisor Susan Phillips for all of her guidance, support, and meticulous editing.Her invaluable feedback and direction pushed me to ask and address new and challenging questions, to take different perspectives, and to create a work that I am truly proud of.I want to thank my committee, Doris Buss and Nathan Grasse.Their ongoing support and their detailed and constructive feedback were critical throughout this process, and particularly in moving this work from the research stage to a complete dissertation.I also want to
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.041 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".