Bibliographic record
Abstract
This article investigates the work of mourning following the terrorist attacks in New York and Washington on September 11, 2001. Combining discussions of mourning, kitsch and sentimentality, I examine the perverse transformation of grief into patriotic nationalism. Linking Freud’s description of mourning as work with Derrida’s articulation of grief as ‘a work working at its own unproductivity’, I explore how grief has been paired with icons of American nostalgia, such as Norman Rockwell, as well as kitschy souvenirs from Ground Zero vendors, and, through this pairing, been transformed into a motive force for war. A central part of this operation, I argue, is the process of identification with the traumatic event. Identification, what Freud describes as a ‘binding force’, takes place across diverse fields - from White House speeches, to kitsch memorabilia made available immediately following the attacks. Identification with the event enables identification with the nation - an operation immediately reifying the official rhetoric of ‘Us against Them’ propounded by President Bush and his advisers. As grief over 9/11 is transformed into a perpetual rationale for war, that day becomes a new origin conveniently obliterating all that came before regarding the history of US nation-building and its own brand of terrorism.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.083 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".