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Record W4321452532 · doi:10.26855/jhass.2023.01.031

An Analysis of Images in <i>The English Patient</i> by Ondaatje

2023· article· en· W4321452532 on OpenAlexaboutno aff
Wenjuan Xu

Bibliographic record

VenueJournal of Humanities Arts and Social Science · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Studies and Diaspora
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyHistoryComputer science

Abstract

fetched live from OpenAlex

The English Patient is one of the representative works of Canadian writer Mikell Ondaatje. The author tries to use archetypal criticism theory to analyze the main archetypal images in this novel, including fire, water, desert and so on. Blending multiple themes is one of the characteristics of Ondaatje's novels, and the English patient is no exception. In the novel, the author skillfully conveys his thoughts and emotions through the characterization of characters, the description of concrete things and the arrangement of plot structure. There is no absolute opposition in Ondaatje's writing. The Barbarians and the civilised, the British and the Indians, the present and the past, chaos and order, the desert and the villa, water and fire, Elmasy and Catherine versus Lao Kip and Haner, they overlap and diverge. Almasy's desperate attempts to erase his nationality, Kip's desire to give up his cultural identity and follow the west, two couples struggling in different situations, and so on, these people's efforts in the face of reality has always seemed so pale. These images seem to be fragmentary, but they are related to each other. They bring together the theme of the novel and convey the author's deep understanding of the disasters that war brings to mankind, and the desire and belief of multi-cultural co-existence.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.624

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 studies0.0010.001
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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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
Published2023
Admission routes1
Has abstractyes

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