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Record W3106911422 · doi:10.1386/fict_00013_1

Literary form, hierarchies and the meeting of two plots in Patrick Gale’s ‘A Slight Chill’

2020· article· en· W3106911422 on OpenAlexaff
Tom Ue

Bibliographic record

VenueShort Fiction in Theory and Practice · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsDalhousie University
Fundersnot available
KeywordsForegroundingVampirePlot (graphics)RomanceValue (mathematics)AffordanceSociologyCategorizationLiteratureArtAestheticsHistoryEpistemologyPsychologyComputer sciencePhilosophyCognitive psychologyMathematics

Abstract

fetched live from OpenAlex

This article incorporates Gerald Prince’s and Caroline Levine’s work on form to reveal some of the innovations in Patrick Gale’s ‘A Slight Chill’ ([1996] 2018). This short story juxtaposes two antagonistic plots: the vampire Lotta Wexel’s gastronomic activities and her teacher Angel Voysey’s romance. By attending to, and drawing connections between, smaller forms (e.g. allusions and metaphors) and larger ones (plots and genre), I argue that we may better understand Gale’s project. Lotta’s plot effectively exposes and frustrates Angel’s. In foregrounding such interactions, he encourages the reader to reassess both the affordances and the inadequacies of the models and expectations that Angel inherits. This article goes on to analyse Gale’s screenplay for his upcoming film adaptation to show how he gives a new application to his earlier project. If the short story is particularly invested in the decisions before Angel, then the screenplay explores, even more so than its source material, how and why we categorize characters into hierarchical forms. This article contributes to knowledge, then, by examining Gale’s writing programme, which has received inadequate scholarly attention; by illuminating some of its complexities; and by demonstrating the value of thinking about the short story in terms of form.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.268
Teacher spread0.248 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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