An Autoethnographic Therapeutic Performative Inquiry on My Lived Experiences as a Black Woman Co-victim of Homicide in Canada
Bibliographic record
Abstract
“Anansi Came” was an autoethnographic therapeutic performative inquiry that explored my lived experiences as a homicide co-victim (Center for Victim Research, 2020) of Afro-Caribbean descent and the impact of “cumulative exposure to lifetime adversity” (Silver et al., 2021, p. 5). Informed by my practice as a dramatherapist and registered psychologist and safeguarded by the best practices of trauma work, this research engaged in a process that combined dramatherapy and performance autoethnography with the principles of engaged pedagogy. The script and performance that I created through this process were the focus of my dissertation. Two questions framed this research: (1) What are my lived experiences as an Afro-Caribbean woman who has survived the loss of a loved one to homicide? (2) How might an embodied storytelling approach, adapted from The Story Within (Silverman, 2020) process, assist me in my healing process, with the potential to encourage others to explore their traumatic experiences? Data within my personal stories, the script I developed and performed, and the audiences’ post-performance feedback uncovered how engaging in embodied storytelling can promote self-discovery, resilience, and trauma recovery. Using dramatherapy strategies provided me with the opportunity to develop the necessary skills to identify misinformation and myths society communicated to me about being a Black homicide survivor. The Story Within process was instrumental in reclaiming my voice by building internal resources that helped me manage my somatic (body-based) experiences and process habitual trauma-related responses (Ogden, Paine & Fisher, 2006) within a safe communal healing space. By disrupting the silencing and dismissal of my experiences as a Black co-victim of homicide and fostering a sense of belonging, my embodiment as a practice and pathway to healing has enabled me to regulate a nervous system impacted by years of oppression. While the focus of this study is on understanding my experiences and assisting my healing journey, my larger motivation for this inquiry is my hope that it will empower and inspire others in my community—especially the youth and educators who have experienced trauma—to go on their own journeys of self-exploration, empowering them to sever the cycle of violence and overcome the effects of trauma.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".