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Record W3155917114 · doi:10.2478/stap-2020-0022

Taking Root in Floating Cities – Space, Environment, and Immigrant Identity in Kerri Sakamoto’s<i>Floating City</i>

2020· article· en· W3155917114 on OpenAlexaboutno aff
Joanna Antoniak

Bibliographic record

VenueStudia Anglica Posnaniensia · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeSociologyIdentity (music)Place identityImmigrationSense of placeGender studiesPlace attachmentSpace (punctuation)National identityAestheticsSocial psychologyLawPsychologyPoliticsLiteraturePolitical scienceSocial scienceEcologyArtPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Abstract Human identity is shaped not only by culture, but also by nature – the environment in which people grow up and live, the places and spaces they visit, work in, and pass on an everyday basis. This people-place bond is particularly important in case of immigrants who are forced to abandon the places they know for a new – and often hostile – environment. This connection between space, environment, and immigrant identity is explored by Kerri Sakamoto, a Japanese-Canadian writer, in her newest novel, Floating City (2018). Focusing on the family narrative of the Hanesakas – and, in particular, the story of Frankie, the oldest son of the family – Sakamoto tells the story of shaping identity through forming a connection with the environment and architecture. The aim of this article is to discuss the way in which Sakamoto presents the people-place bond and its impact on immigrant identity as represented by the connection of the Japanese-Canadians with four elements: water, air, earth, and fire. Furthermore, the article analyses Sakamoto’s version of an alternative history of Toronto and the possible solutions to the current environmental crisis it brings. For this purpose, the author uses a mixture of methodological concepts stemming from postcolonial theory and environmental psychology, such as homing desire, rootlessness, place attachment, non-place, and the people-place bond.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.029
GPT teacher head0.300
Teacher spread0.271 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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