arts-informed 2 An Inquiry into Symbolic Violence and Silencing: The Good Christian
Bibliographic record
Abstract
inquiry into marginality & resistance, and the creative-research process. Although an autoethnographic novel with a journal style, inspired by Jean-Paul Sarte's Nausée (1964), it contains several short stories (Gosse, 2003). The Good Christian Woman is one of these. My thesis research has queer, poststructuralist underpinnings, and is purposely oppositional. Violence can be symbolic, and manifest in religious, educational, medical, legal, familial, and social institutions and settings. Violence can take many forms- verbal, physical, and psychological. Silencing, a symbolic and pervasive form of violence that many of us experience, causes no physical scars, but the psychological ramifications can be long term and significant (Gosse & Gearson, 2002; Gosse, Labrie, Grimard, & Roberge, 2000). I seek to examine who and what is silenced- when, where, and why? I have used the writer-ethnographer's approach in creating this fictionalized short story, borrowing from moving personal experiences and feelings from my own life history. Writer-research and reader-researcher interpenetrate one another through the medium of the text. Expressing these feelings, and disruptive moments, is a bridge to breaking patterns of silencing in my life, and towards helping others-readers, enter into my experiences, so that we might together critically broaden our understandings of life and culture (Eisenhart, 2001). The musings on religion are inspired from sermons from the Metropolitan Community Church of Toronto
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".