Engendering Biopoetics of Testimony: Louise Dupré, Chus Pato, and Erín Moure
Bibliographic record
Abstract
The act of bearing witness to the remnants of Auschwitz strains poetry and poetics. To examine manifestations of this disarray, this article first establishes a dialogue between philosophy and poetry by discussing Giorgio Agamben’s conception of testimony and Jacques Derrida’s reflection on the shibboleth. It goes on to consider writings by Louise Dupré, Chus Pato, and Erín Moure, who write as inheritors of necropolitical violence, yet at a remove from the Shoah. Although their writing practices cross paths with Agamben’s and Derrida’s reflections, these poets generate a biopoetics of testimony that exceeds these reflections by engendering a tension between dispossession and regeneration.L’acte de témoigner des vestiges d’Auschwitz met à mal la poésie et la poétique. Pour examiner les manifestations de ce désarroi, cet article établit d’abord un dialogue entre philosophie et poésie en abordant la conception du témoignage de Giorgio Agamben et la réflexion de Jacques Derrida sur le shibboleth. Il se penche ensuite sur les écrits de Louise Dupré, Chus Pato et Erín Moure, qui écrivent en héritiers de la violence nécropolitique, mais à distance de la Shoah. Bien que leurs pratiques d’écriture croisent les réflexions d’Agamben et de Derrida, ces poètes génèrent une biopoétique du témoignage qui dépasse ces réflexions en engendrant une tension entre dépossession et régénération.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".