Encountering violence : the stories of gender nonbinary Indigenous, Black and people of colour (IBPOC)
Bibliographic record
Abstract
My research examined gender nonbinary Indigenous, Black and people of colour (IBPOC) experiences with violence and answers two questions: 1) How do you perceive and understand violence? and 2) How does violence affect your relationships (e.g., friendships, family, intimate partner(s) or colleagues and supervisors)? Participant responses filled in gaps in the literature and provided cultural recommendations on changing how cisgender white groups receive, interpret, and express knowledge on the stories of gender nonbinary IBPOC encountering violence. My theoretical framework includes intersectionality, queer of colour, and trans of colour critique and identified patterns that emerged from the information collected. I reviewed literature by scholars whose work is grounded in gender, race, sexuality studies, including Atmospheres of Violence by Eric A. Stanley (2021), The Sense of Brown by José Esteban Muñoz (2020), Trans Exploits by Jian Neo Chen (2019), The Colonial Problem by Lisa Monchalin (2016), Violence Against Queer People by Doug Meyer (2015), and Aberrations in Black by Roderick A. Ferguson (2004). The work of these scholars supported my literature review and information analysis process. I conducted four semi-structured interviews in the format of storytelling online over Zoom. This study interpreted the experiences of one Indigenous (not specified), Black (Somalian), Middle Eastern (not specified) and agender neutral/non gender affirming participant; one Black (Puerto Rican-American) and androgynous/trans/non-binary participant; one Indigenous (Métis) and trans-mask/nonbinary/left-of-centre leaning participant; and one person of colour (South Asian-European) and nonbinary; three of who had a university degree. All four participants experienced interpersonal and collective violence, and one of the encountered self-directed violence. The study findings revealed challenges amongst participants of intersectional identities and their experiences with violence. The four key themes that emerged from this research are 1) microaggressions 2) gender-based violence, 3) family and relations, and 4) workspace and educational settings. The analysis of findings and key themes are detailed in the discussion chapter and supported by the theoretical framework and research literature.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 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".