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Record W7063918600

Apocalypses Now: Two Modes of Vulnerability in Last Night and The Mist

2018· article· en· W7063918600 on OpenAlexaboutno aff

Bibliographic record

VenueRepositorio Institucional de la Universidad de Huelva · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsVulnerability (computing)NarrativeRhetoricArgument (complex analysis)PoliticsThematic analysisSocial vulnerability
DOInot available

Abstract

fetched live from OpenAlex

This paper draws on Judith Butler’s notions of vulnerability, precarity, and grievability to examine two filmic texts: the Canadian Last Night (Don McKellar, 1998) and the American The Mist (Frank Darabont, 2007). Both primary sources feature the apocalypse as their principal narrative and thematic concern –a trope virtually unexplored from the standpoint of the production of vulnerability and the bodily dimensions of political and ethical life. In the present contribution I conduct a close analysis of both films so as to identify and evaluate the significantly contrasting modes of vulnerability produced in these two narrations. I argue that these conflicting worldviews originate from the differentiated episodes of (de)valuation, legitimization, and recognition experienced by and in bodies in the face of the ultimate phenomenon of vulnerability: the apocalypse. My structuring argument is that Last Night complies with the notion of vulnerability as a locus of ethical cohabitation and affective engagement while constructing a heterogeneous sense of Canadianness. The Mist, on the other hand, deploys vulnerability as a discursive mechanism that causes individual and social bodies to be subjected to a range of violence-prone asymmetries and processes of dehumanization, rearticulating key rhetoric and imagery from American cultural history

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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