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Record W4280572667 · doi:10.3138/cras-2021-014

<i>Mad Men</i> and the Falling Bodies of 9/11

2022· article· en· W4280572667 on OpenAlexaffvenue
Daniel Adleman, Chris Vanderwees

Bibliographic record

VenueCanadian Review of American Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUncannyFalling (accident)Falling in loveSynecdocheRepresentation (politics)PoliticsContradictionSociologyHistoryAestheticsLiteratureLawPhilosophyArtPolitical sciencePsychologyMetonymyEpistemology

Abstract

fetched live from OpenAlex

In this article, Daniel Adleman and Chris Vanderwees explore the unique manner in which AMC’s Mad Men remediates traumatic images of the 11 September 2001 terrorist attack on Manhattan’s Twin Towers. The authors take up the production of the show’s title sequence, the media controversy surrounding its representation of 9/11’s falling bodies, and Don Draper’s perilous trajectory over the course of the series. Their analyses lead Adleman and Vanderwees to dwell on Mad Men’s portrayal of the advertising industry as a crucible of, and synecdoche for, unfettered capitalism. The authors contend that Mad Men’s falling man sequence ultimately serves as an uncanny commentary on American political anxieties, contradictions, and uncertainties at the beginning of the twenty-first century.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.026
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.050
GPT teacher head0.279
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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