The lexicon of mainstreaming equality: Gender Based Analysis (GBA), Gender and Diversity Analysis (GDA) and Intersectionality Based Analysis (IBA)
Bibliographic record
Abstract
In the last 15 years, much debate has ensued at the international level regarding gender mainstreaming (GM), its efficacy and future utility. In Canada, similar discussions have taken place where GM has largely been operationalized in the form of gender-based analysis (GBA). However, there has been a lack of clarity regarding the ways in which GBA as a conceptual framework compares to other approaches available for working towards equality in public policy, namely gender and diversity analysis (GDA) and intersectionality-based analysis (IBA). As a result, the potential of these models to respond to diversity and inequality, especially GBA and GDA, are often overstated and/or conflated. The purpose of this paper is to elucidate the similarities and differences between GBA, GDA, and IBA. This analysis illuminates the strengths and limitations of these types of approaches, especially in terms of how each conceptualizes and is able to address a wide variety of diversities among the Canadian population. This paper argues that only IBA is flexible enough to capture the multidimensional nature of oppression and discrimination because it disrupts the systematic prioritization of gender as a starting place for assessing experiences of inequality.
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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 | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.010 | 0.088 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".