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Record W3139185142 · doi:10.1386/host_00027_1

The zombie in the grey flannel suit: Romero’s classic Dead trilogy and metaphors of mass subjectivity

2021· article· en· W3139185142 on OpenAlexaff
Nathan Rambukkana

Bibliographic record

VenueHorror Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsZombieSubjectivityComicsTrilogyArtUncannyLiteratureAestheticsArt historyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

This article explores the relationship between zombies and ‘mass subjectivity’ through examining the motifs, as well as the critical and scholarly reception, of Romero’s classic Dead movies and their successors. Contrasting the ‘fast zombies’ of later films, Romero’s zombies are withered and decayed versions of everyday people: tattered and frayed at the edges, their colours muted, their skin and clothing rendered in greyed-out tones. They are the mundane dead, animated. Romero’s filmic horror taps an uncanny rendering of the everyday. Gardens, streets and malls are made strange by the homogeneous mob progressing in endless lines, murmuring incoherencies and striving to just be. We can locate the visual character of the zombie within a genealogy of metaphors of mass subjectivity such as the man of the crowd, the badaud figure, constantly searching for a place, but symbolically disarticulated. By considering the sometimes comic, sometimes tragic and often horrific Romero zombie in a lineage of visual and literary figures linked to mass subjectivities – the man in the suit, the monstrous man, the man of the crowd, the badaud – this article answers the question: What does thinking about the relationship between the Romero zombie and mass subjectivity enable us to do, think or observe?

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.019
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.343
Teacher spread0.297 · 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 designQualitative
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

Citations4
Published2021
Admission routes1
Has abstractyes

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