Changing the Memory of Suffering: An Organizational Aesthetics of the Dark Side
Bibliographic record
Abstract
This paper addresses processes of subjection and abjection as expressed in organizational and collective memory. It complements recent developments in organizational memory studies by demonstrating how the dark side of organization has been subjected to what Susan Sontag calls a ‘collective instruction’ process that normalizes how this dark side is understood, or marginalized. The paper argues that history today is often represented as kitsch and offers a method of aesthetic ‘juxtaposition’ of visual artefacts that together with a detailed reading enables researchers to critically challenge this organization of memory and reintegrate abjected material. The method is exemplified by juxtaposing the iconic World War II photo of a little Jewish boy leaving his home with his hands in the air during the Nazi clearances of the Warsaw Ghetto and Paul Klee’s iconic painting of an angel in terror, Angelus Novus, painted in 1920 just after World War I. The analysis demonstrates how history tends to be organized by a majoritarian system – in this case what has been termed ‘the Holocaust industry’ – through collective instruction in how to interpret events, and outlines alternative ways for exposing and resisting this process, resulting in the creation of counter-narratives. This analytical strategy confirms that organizational aesthetics resides at the heart of what is political.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".