Dot-Dot-Dot: A Feminist Critical Poetic Inquiry of Silence in Teacher Candidates’ Responses to Teaching Sexual Assault Narratives
Bibliographic record
Abstract
This project emerges from a larger feminist study where 23 teacher candidate participants took up reading a trauma text set of sexual assault literature and responded to pedagogy for teaching such narratives with adolescents in Canadian K-12 public schools. This critical feminist poetic inquiry (Faulkner, 2016; 2018a; 2018b; 2020a; 2020b; Ohito & Nyachae, 2018; Prendergast, 2015) represents a significant piece of this project: how breath, pauses, slivers of silence(s), and slow pacing surfaced during teachers candidates’s disclosures of violence while discussing their learning about the pedagogical potential of Tarana Burke’s MeToo movement, centering sexual assault narratives in the English literature classroom, and resisting rape culture(s). Because participants’ testimonies of diverse trauma experiences demanded poetry of witness (Davidson, 2003), poetic inquiry allowed for attendance to these offerings through the composition of visual ‘silence poems’ that re-transcribe the disclosures by capturing nonverbal moments: gaps, pauses, trailings off, etc. With the aim of thinking ahead to how secondary English teachers might cultivate radical classroom communities prepared to cultivate radical solidarity as resistance to patriarchal violence, this paper explores how poetic inquiry might especially offer a significant methodological entrypoint for antirape research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.027 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".