Transformative organizational and programmatic change? Civil society responses to the Canadian Feminist International Assistance Policy (<scp>FIAP</scp>)
Bibliographic record
Abstract
Abstract Motivation The article aims to examine how Canada's Feminist International Assistance Policy (FIAP) shaped efforts of civil society organizations to address gender equality through organizational and programmatic change. FIAP and other feminist policies have direct implications as to how organizations design and administer their work to address gender inequality and on how funding agencies and foundations administer and support this work. Research Question What are the opportunities and limitations to FIAP's implementation based on the experiences of civil society organizations' efforts to address gender inequality? Approach and methods Data collection for this article took place between 2019 and 2021, beginning two years after the launch of FIAP. This research adopted a mixed‐methods grounded theory approach, where the collected data shaped the conceptual framework. An online survey, interviews, participatory workshops, and media analysis were included in the data collection. Staff from civil society organizations and the University of Ottawa supported the research design process. A response rate of 50% was achieved with a total of 42 respondents out of 83 organizations responding to the survey. A total of 15 interviews were conducted with gender equality specialists based in Canadian international organizations. Findings Analysis from data collected in 2019 with gender specialists and civil society organization (CSO) staff, as well as analysis of media coverage of challenges faced by feminist organizations in 2020 and 2021 revealed that the potential for CSO investment through staff support (financial, training and government guidance) could only be partially realized within the “unfeminist” structural landscape in which development programmes oversee the administration of under‐represented groups. Policy implications This study demonstrates the limitations around building stronger linkages between policy formation and implementation processes, and also the opportunities. The authors argue that strengthening engagement with feminist networks globally could align policy priorities with those identified by grassroots movements, while influencing how funding agencies value feminist practice in CSOs.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".