The Anti-Archive? Claude Lanzmann's <i>Shoah</i> and the Dilemmas of Holocaust Representation
Bibliographic record
Abstract
Research Article| March 01 2007 The Anti-Archive? Claude Lanzmann's Shoah and the Dilemmas of Holocaust Representation Elisabeth R. Friedman Elisabeth R. Friedman York University efriedman@rogers.com Elisabeth R. Friedman is a doctoral candidate in Social & Political Thought at York University in Toronto. She is completing a dissertation titled “Imaginative Archives: Virtual Memory and the Dilemmas of Holocaust Representation,” which examines the status of the archive in imaginative representations of the Holocaust. She currently teaches Cultural Studies at Trent University. Search for other works by this author on: This Site Google English Language Notes (2007) 45 (1): 111–121. https://doi.org/10.1215/00138282-45.1.111 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Elisabeth R. Friedman; The Anti-Archive? Claude Lanzmann's Shoah and the Dilemmas of Holocaust Representation. English Language Notes 1 March 2007; 45 (1): 111–121. doi: https://doi.org/10.1215/00138282-45.1.111 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsEnglish Language Notes Search Advanced Search The text of this article is only available as a PDF. Copyright © 2007 Regents of the University of Colorado2007 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".