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Record W3159222790

Reality in the Margins, Pseudo-Reality in the Main Frame: The Posthuman in Steven Hall’s "The Raw Shark Texts"

2018· article· en· W3159222790 on OpenAlexaff
Shawna Guenther

Bibliographic record

VenueUniversity of Lodz Repository (University of Łódź) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAllegoryReading (process)PosthumanismNarrativePosthumanTextualityLiteratureMedia studiesHistorySociologyAestheticsArtLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

I contend that, at its core, Stephen Hall’s The Raw Shark Texts is an allegory of reading that illustrates how composite realities exist in the increasingly electronically-dominated world of posthumanism. Hall succinctly identifies how words act upon readers intellectually and psychologically. Readers take the written words from the page and turn them into actual people, places, things, and events within their minds, bringing their own past narratives to create their versions of the text’s pseudoreality. However, the text’s main character, Eric, is disabled by his repeated episodes of complete amnesia – his reality is constantly being erased and rewritten, just like computer memory, leaving Eric with no past narrative to inform his present and future. Hall, very much aware of the conflict between reality and pseudoreality, conflates the worlds of written and digital text, and of human and computer memory in ways that both celebrate their coexistence and warn of one’s potential to eliminate the other. Thus, the allegory of reading exemplifies the potential destruction of reading and the end of electronic posthumanism. As digital text and the mainframe threaten to destroy the act of reading in the twenty-first century, the death of the reader looms large.

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)0.004
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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