The lexicon of mainstreaming equality: Gender Based Analysis (GBA), Gender and Diversity Analysis (GDA) and Intersectionality Based Analysis (IBA)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it