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Record W4376458560 · doi:10.7202/1099088ar

Zombie Semiotics and the Economics of the Apocalypse

2023· article· en· W4376458560 on OpenAlexvenueno aff
John Hartley

Bibliographic record

VenueRecherches sémiotiques · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsZombieSemioticsSociologyReflexivityPerspective (graphical)SemiosisEpistemologyMedia studiesSocial scienceArtPhilosophyComputer scienceVisual artsComputer security

Abstract

fetched live from OpenAlex

This paper explores the relations between economics and semiotics, using as its main conceptual lever the figure of the zombie. John Quiggin, Paul Krugman and others criticise the persistence of ‘undead ideas’ in economics. The paper applies this perspective to urban semiosis, where undead monuments have become key sites for staged conflict across the world. Working from Doru Pop’s critique of ‘zombie semiotics’, the paper turns from fiction to the realities of the digital semiosphere and technosphere. Using Juri Lotman’s model, it shows how the climate and coronavirus crises are globally mediated but not self-regulated. It identifies reflexive autocommunication as the means for semiospheric response to planetary crises. Zombie economics and conflict media spread fear of automation and ‘useless’ populations, while techno-entrepreneurs plan to abandon the planet to the apocalypse. It is left to autocommunication among teenage activists to contest the real zombies.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0050.042
Scholarly communication0.0060.006
Open science0.0000.004
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 head0.126
GPT teacher head0.373
Teacher spread0.247 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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