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Record W2801478467

“The Function of Urban Space in "In the Skin of a Lion" by Michael Ondaatje”

2017· article· en· W2801478467 on OpenAlexaboutno aff
Jakhan Pirhulyieva

Bibliographic record

VenueBern Open Repository and Information System (University of Bern) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)Character (mathematics)SloganNarrativeIdentity (music)AestheticsFunction (biology)HistorySociologyLiteratureArtLawPhilosophyPoliticsLinguisticsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The slogan of Tourism Toronto today “The World in One City” explicitly signifies the city’s multinational nature, its new identity as a “polyphonous city of many cultures” (Rosenthal 2011, p.32). The city of Toronto, its history in the first half of the twentieth century and the numerous people behind that history, whose names will remain either unknown or long forgotten, play a substantial role in Michael Ondaatje’s novel In the Skin of a Lion. The space of the city is not only an important background where the majority of the events unfold for the reader, but it is also a driving force in both the construction of the story’s narrative and the development of the characters. Drawing mainly on the works of Henri Lefebvre and Edward Soja, this paper investigates the function of urban space in Ondaatje’s novel In the Skin of a Lion, and further contemplates the significance of urban space in modern literary texts. The urban space incorporates several functions in the text. On the one hand, the multifaceted character of the city’s vast space isolates and alienates the individuals who live there. On the other hand, it unites the characters in a paradoxical way and bonds them together to a certain degree. Furthermore, the urban space is constantly produced by the people who live there; and every character in the novel constructs their own space of Toronto and shares their individual space of the city with the reader.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.804

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 studies0.0010.000
Scholarly communication0.0000.002
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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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
Published2017
Admission routes1
Has abstractyes

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