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Record W7056513093

Exploring the sense of belonging and the notion of home in Margaret Atwood’s Cat’s Eye

2014· article· en· W7056513093 on OpenAlexaboutno aff

Bibliographic record

VenueUniversiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · 2014
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBelongingnessMaslow's hierarchy of needsAlienationHierarchySense of placeOrder (exchange)Psychology of selfCommon sense
DOInot available

Abstract

fetched live from OpenAlex

Human beings need to associate and mingle with their surroundings, be they the family, neighbours, colleagues, nature or a place, in order to feel attached and belonging to a particular society and its environment. This article explores the concept of a sense of belonging in Margaret Atwood’s novel Cat’s Eye (1988). The story is about the protagonist, Elaine, revisiting her childhood memories, where she learned about friendship, longing and betrayal. Although she was being bullied by her own best friends, Elaine remained with them as she feared being alienated. Despite the many years spent outside Toronto and away from her sad childhood memories, Elaine still felt that her hometown was her real home. The notions of belongingness used in this analysis are aided by Abraham Maslow’s Hierarchy of Needs and William Glasser’s Choice Theory. Elaine’s strong attachment to her hometown and her childhood memories is due to the human needs for love and belonging and in an attempt to evade alienation and loneliness. Parallel to what Maslow defines as a sense of belonging, humans on a very basic level long for belonging, respect and love, and Elaine’s actions are seen as a desperate attempt to get through her days in the way that Glasser outlines in Choice Theory – the need for love and belonging is closely linked to the need for survival.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.173
Teacher spread0.162 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

Explore more

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