From Reena to Beti : a counterstory considering structural racism and limitations in feminist nonprofit organizations
Bibliographic record
Abstract
Adding to a growing field of literature in critical race studies in education, and gender studies, this project looks to understand cracks in feminist nonprofit organizations, specifically as they relate to services offered for racialized and Indigenous girls and women. Using data from 15 interviews with racialized and Indigenous activists with experience in mainstream nonprofit feminist organizations on unceded Coast Salish territory in the Greater Vancouver area, I compile the activists’ experiences in a composite counterstory drawing upon critical race theory methodologies (Solorzano & Yosso, 2001, Solarzano & Yosso, 2002, Duncan, 2002, Cook & Dixson, 2012). Beti, the protagonist of the counterstory, reveals the many structural barriers that exist within these organizations. This includes: tokenized use of racialized and Indigenous bodies to hold strategic positions maintaining “diversity” projects or fulfilling well-intentioned organizational policies only to come up against longstanding institutional barriers committed to racist and colonial white settler structures. This research indicates that these organizations had and continue to have a longstanding history of maintaining the nonprofit industrial complex. Beti, as a racialized settler, centers Indigenous ways of knowing, such as critical place inquiry, to better understand her position on stolen territories and how activism on this land might impact her ability to effect change because of the very nature of racialized and gendered violence that persists within the changing landscape of the city of Vancouver. Finally, I look at the ways this research project is incomplete. Additional research is required to further understand the experiences of activists in organizations, barriers to access and systemic exclusion for racialized and Indigenous girls and women within institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".