Imaginer, monter : la mémoire inachevée d’Auschwitz selon Georges Didi-Huberman
Bibliographic record
Abstract
Écorces de Georges Didi-Huberman est le « récit-photo » d’une déambulation à Auschwitz-Birkenau en juin 2010. Il y interroge quelques lambeaux du présent qu’il fallait photographier pour voir ce qui survit de la mémoire d’Auschwitz, ce qui pourrait mettre en œuvre le désir de ne pas en rester au deuil crispé du lieu. Cet article explore l’inachevé comme métaphore de l’écorce : d’une part, un morceau d’arbre, un « bout de texte », un fragment ; et d’autre part, une image, une surface pelliculaire qui ne couvre pas, mais se détache d’un corps. Qu’est-ce qu’on peut attendre d’une photo d’un morceau d’écorce de bouleau dans un endroit comme Birkenau ? J’analyse ainsi la question du montage entre texte et image, du partiel, de la production de « petites vérités » en tant qu’inachèvement d’un espace mémoriel. To Imagine and to Mount: the Unfinished Memory of Auschwitz according to Georges Didi-Huberman ABSTRACT Écorces by Georges Didi-Huberman is the “récit-photo” of a wandering experience through the camps of Auschwitz-Birkenau in June 2010. The author takes photographs of some pieces of birch bark and reflects on what remains of Auschwitz today. Does this gesture have the power to shift him from mourning? In this article, I study the “inachevé” (the act of un-finishing) as a metaphor of the birch bark: on the one hand, this is a small part of a tree, a part of a text, a fragment; on the other hand, this is an image in our memory, a precarious surface that is detached from a body (of a tree). What to expect of a photographed piece of birch bark in such a place like Birkenau? I thus analyze the question of mounting (le montage) pieces of text and images, the idea of the partial and the production of “small truths” as figures of the unfinished space of our memory.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".