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Record W4283396515 · doi:10.4312/elope.19.1.93-106

Adverbials of Time, Time Expressions and Tense Shifts in Alice Munro’s “Dance of the Happy Shades”

2022· article· en· W4283396515 on OpenAlexaboutno aff
Katja Težak

Bibliographic record

VenueELOPE English Language Overseas Perspectives and Enquiries · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsAlice (programming language)DanceContext (archaeology)Present tenseLinguisticsPlot (graphics)ArtLiteraturePsychologyHistoryArt historyVerbPhilosophyMathematics

Abstract

fetched live from OpenAlex

Alice Munro, the first female Canadian to have been awarded the Nobel Prize for literature, is a literary challenge and delight for the reader also with regards to the usage of references to time and their linguistic construction. Her literary work comprises short stories and one novel, which can more accurately be described as a short story cycle. She takes interest in everyday, small-town life and the human relationships in it, all described in concise, down-to-earth language. The settings in most of her stories are limited communities in a typically Canadian context. This paper deals with her short story “Dance of the Happy Shades” from the collection of the same name (1968), and focuses on a stylistic analysis of time expressions, tense shifts and adverbials of time. Munro has been praised for constructing moods of familiarity, home and small-town safety. The paper attempts to show that she achieves this not solely through the plot and themes, but also with the meticulous care with which she uses time expressions, time adverbials and tense shifts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.200
Teacher spread0.196 · 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.

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
Published2022
Admission routes1
Has abstractyes

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