Gender-based analyisis plus (GBA+) and Intersectionality: Overview, an enhanced framework, and B.C. Case Study
Bibliographic record
Abstract
In this paper, we present an overview of GBA+ and its central components, as well as a case study application of the framework to the question of poverty in the British Columbia context. We begin by tracing the theoretical foundations and development of SWC’s GBA+ tool, touching on the relevance of the framework given broader government goals of diversity, inclusion, and inclusive growth. Next, we consider the limitations and potential of GBA+ as operationalized in Canada, and then build on this analysis to adjust the existing GBA+ tool, with the goals of better incorporating the concept of intersectionality and rendering the framework useful beyond governmental contexts. Finally, we apply relevant elements of the adapted framework in a case study, examining the issue of poverty in B.C. from a gendered and intersectional perspective. Our main finding is that exploring the nature and causes of poverty in B.C. results in a harrowing picture, both of need and oppression, and one that government systems have been complicit in constructing. As a result, the BC Government will need to implement GBA+ frameworks within a context that includes broader reconsiderations of government process, structures, institutions, and norms, with an aim to remove discrimination and bias (e.g., heteronormativity, colonialism, misogyny, ableism). Ultimately, an understanding of both the broad context of systemic pathologies and the barriers associated with intersecting identity factors and social positions that shape individual experiences will be integral for analysts hoping to advance agendas of diversity, inclusion, and poverty reduction, particularly through the development of public policy.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".