Resistance education: African/Black women shelter workers' perspectives
Bibliographic record
Abstract
The silencing and exclusion of differently located African/Black women shelter workers' voices and experiences in the woman abuse community in Toronto, Ontario, Canada, was the catalyst for this research. This dissertation examines the lived and worked experiences, and resistance of African/Black women shelter workers. It focuses on 18 differently located African/Black women who worked in shelters in Toronto between 1995 and 2005 during governmental cuts and organizational restructuring processes. This study re-centers and rehistoricizes these often-missing stories of African/Black women, their indigenous feminist knowledges, and their activism. The purpose of this study was to examine intersectional violence and resistance in woman abuse shelters. African/Black feminist, anti-colonial theorization and praxis are critical for resistance against intersectional forms of violence including racism, sexism, heterosexism, classism, ableism, ageism, and other interlocking oppressions. "Resistance Education", a qualitative methodology developed and utilized in this study, provided an integrated, feminist/womanist, anti-colonial, anti-racist, analysis and praxis. The seven features of this methodological approach challenge traditional notions of Eurocentric "objective" research by re-centering the standpoint of African/Black women's voices, as well as the voices of other oppressed groups, in both research and education. This methodology, incorporating art-based strategies of resistance and activism, formulates and utilizes a method called "Creative Resistance". Collective witnessing, consciousness raising/sharing, and collective action provided individual and collective locations, identities, and agency, which supported revolutionary social change and transnational feminist actions. The "Identity Trichotomy" discussed in this study reveals the complexities of essentialized derogatory sameness, heterogeneous identities, and collective resistance. This research study contributes to feminist historical analysis of the larger violence against women communities as it provides the missing voices and stories of African/Black women shelter workers. It also adds innovation to qualitative methodology by providing another approach to doing feminist, anti-colonial, anti-racist, art-based research and resistance. This dissertation in itself is an act of resistance and revolutionary agency.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".