Powerful Silences: Becoming a Survivor Through the Construction of Story.
Bibliographic record
Abstract
Survivors’ accounts of traumatic events function on many levels for both the teller and the hearer. By giving voice to what has been silenced, testimonies to the lived experience of trauma challenge dominant perspectives on the meaning and significance of both historical and contemporary events. The construction of these stories and their telling may also provide a means of countering the devastating psychological effects of the trauma. This paper will explore one story about the Turkish genocide of Armenians in 1915 as told to me by my grandmother, Elmas Tutuian. Remarkably consistent over the years of its telling, Tutuian’s story omits as much as it tells. Examining this narrative from both a psychological and a textual perspective, I suggest that by choosing to be silent about parts of her experience, Tutuian constructed herself as a survivor rather than a victim. In choosing to tell her narrative to me, she also shaped my sense of resistance to oppression. The article references works analyzing survivors’ accounts of trauma from a literary, psychological, sociological, theological, and historical perspective.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".