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Record W1858513037 · doi:10.5539/ells.v5n3p76

Sinifying Joyce: Appraising Second-Language Confucian Readers’ Constructions of Meaning in Ulysses

2015· article· en· W1858513037 on OpenAlexvenueno aff
C. A. DeCoursey

Bibliographic record

VenueEnglish Language and Literature Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)Reading (process)NarrativeConstruct (python library)PostmodernismLinguisticsIdentity (music)Character (mathematics)Computer scienceLiteraturePsychologyAestheticsArtPhilosophy

Abstract

fetched live from OpenAlex

This paper explores how Chinese students construct the meaning of Joyce’s Ulysses. Second-language learners are known to use reading strategies with difficult texts such as Ulysses. This study of 157 undergraduate Chinese nonspecialists who read parts of Ulysses in an English literature general education course explores how Confucian values shape Chinese readers’ responses to Ulysses, in both reading strategies and content areas. Survey data was collected using a 4-point Likert scale. Qualitative data was gathered using six appraisal terms. Student reflections were analysed using Corpus Tool. Results indicate the primacy of culture in shaping individual readings. Second-language learners use metacritical reading strategies in making sense of this difficult text. However, the inclusion of culturally challenging material, and complex interiority and narrative moved these proficient second-language readers beyond their usual concerns with accuracy of translation and synthetic overviews of complex plot and character detail, towards literary reading. The challenges of Ulysses as a text induced appreciation of unreliability in narration, postmodern theories of meaning, and the disjunctures and discontinuities of the self, expressed in interior monologues, and disqualified in the social performance of identity.

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 score0.331
Threshold uncertainty score0.563

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 communication0.0000.001
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 head0.051
GPT teacher head0.308
Teacher spread0.257 · 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
Published2015
Admission routes1
Has abstractyes

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