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Record W4390341484 · doi:10.5430/wjel.v14n2p83

Spatial Identity: Identity through Memory and Space in John Banville’s The Sea

2023· article· en· W4390341484 on OpenAlexvenueno aff
Stephen Samuel A, Evangeline Priscilla B

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)NarrativeIdentity (music)Theme (computing)Reflexive pronounSpace (punctuation)Sense of placeAestheticsRealization (probability)SociologyHistoryChildhood memoryLiteraturePsychoanalysisComputer sciencePsychologyPhilosophyArtLinguisticsWorld Wide WebEpisodic memory

Abstract

fetched live from OpenAlex

John Banville’s The Sea (2005) has its entire story drawn on memory. Through a close textual analysis, the paper examines the novel through the framework of memory. The paper is structured around the ideas put forth by the philosopher Edward S. Casey and examines how memories play a significant role in forming an identity within the individual and how memories are formed from spaces. Banville through his narrative techniques through the character indirectly presents the reality of the world. His withdrawal into the past by Max in the novel is not part of a theme but rather a unique narrative strategy employed by Banville. An individual has a sense of connection to their world with frequent interactions with the spaces around them. The research investigates how the spaces instigate the journey to the past in the character Max Morden. The sea and the house where Max spent his childhood are the spaces that aid in giving a sense of identity to Max who feels lost after the death of his wife. In a journey of searching for himself, he ends up visiting the place where he spent a holiday vacation during his childhood. It is through the engagement with these spaces and recollected memories from them that the character Max ultimately comes to a self-realization of his lost identity and in the end feels a sense of belonging to the world.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.313
Teacher spread0.291 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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