An Early Exploration of Evaluation Practices under Canada’s Feminist International Assistance Policy
Bibliographic record
Abstract
The newly released Feminist International Assistance Policy (FIAP) aims to gender mainstream all stages of Canada’s international development projects with the goal of using a “truly feminist approach that supports the economic, political and social empowerment of women and girls, [making] gender equality a priority, for the benefit of all people” (Government of Canada, 2017). However, it is not clear what comprises a “truly feminist approach” in the policy or how this is being applied in the field. This thesis therefore aims to assess what feminist approaches can be found within the evaluations of projects that have been conducted under the FIAP and what type of feminism they portray. The methodology consisted of examining three case examples through a document review of key evaluation materials, an interview with a representative from each organization, and a ‘Feminist Evaluation Scorecard’ to summarize the findings. The analysis of these qualitative methods demonstrated that the evaluations, although found to be using some feminist approaches, align better with a technocratic version of feminism rather than a transformational one. It was also found that the participating organizations have limited knowledge of the FIAP and face barriers in implementing feminist approaches within their evaluation work. Some suggestions for future practice were provided including increasing clarity in the wording of the FIAP, providing additional resources to organizations through training and funds so that they may better implement feminist evaluations, and increasing overall communication on expectations so that a “truly feminist” approach may be used in evaluations going forward.
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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 | 0.269 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.026 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".