Buzzwords and fuzzwords: flattening intersectionality in Canadian aid
Bibliographic record
Abstract
In 2017, the Canadian Liberal government introduced the Feminist International Assistance Policy (FIAP), which offers an “intersectional” lens by taking into consideration the diversity of women and girls. This article argues that “intersectional” conceptualized inconsistently in FIAP, and outlines the dissimilar understandings of intersectionality between GAC officials and civil society members. The lack of clarity around intersectionality is predictable given that this issue fuels debates and dialogues in the field of intersectionality theory. As intersectionality has traveled from activist circles to academia and beyond, the theory of intersectionality has often been reduced to a fuzzword and buzzword. Ultimately, this article argues that the impact of this theory on Canadian aid may be quite limited if intersectionality is not clearly and consistently conceptualized.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.048 | 0.053 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".