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Record W1892786435

Narrative Time and Speculative Fiction: Reflection of Social Conditions in Temporal Implications

2011· article· en· W1892786435 on OpenAlexvenueno aff
Soe Marlar Lwin

Bibliographic record

VenueStudies in literature and language · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeTemporalityPlot (graphics)LiteratureFantasyNarrative psychologyPostmodernismNarrative networkRealismClockworkNarrative structureHistoryAestheticsEpistemologyNarrative historyPhilosophyNarrative criticismArt
DOInot available

Abstract

fetched live from OpenAlex

Speculative fiction, with elements of fantasy integral to narrative, has developed as a literary genre with some underlying postulates and textual strategies that challenge the boundaries of narrative realism. It is often examined as a search for the definition of human beings and their status in the universe and an impact of scientific and technological advances upon human beings. It is believed that the world view created by development of science and technology at a particular period influences the choices made for various narrative devices, such as point of view, narrative time sequence, plot structure, character and language in speculative fiction. This paper examines temporal implications of the plots in two speculative fiction novels, Anthony Burgess’s A Clockwork Orange (1972) and William Gibson’s Neuromancer (1984), written in two different periods which are about ten years apart. Using Ricoeur’s (2002) distinction between episodic and configurational dimensions for the conception of time, the paper aims to uncover different ways of using narrative temporality in the configuration of plots in these two novels, and relate these differences to broader social conditions happening at the two respective stages in the postmodern age. Key words: Narrative Time; Plot; Temporal Implications; Social Conditions, Speculative Fiction

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.325

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 head0.041
GPT teacher head0.325
Teacher spread0.284 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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