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Record W2409641907

Hunger, Consumption, and “Contaminated” Aesthetics in Rawi Hage’s Cockroach

2014· article· en· W2409641907 on OpenAlexaffvenueabout
Justyna Poray-Wybranowska

Bibliographic record

VenueStudies in Canadian Literature · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork University
Fundersnot available
KeywordsAestheticsContext (archaeology)Environmental ethicsGentrificationSociologyConsumption (sociology)BeautyRhetorical questionHistoryArtLiteratureArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This essay reads Rawi Hage’s Cockroach as a novel that goes beyond revealing the alienation and personal difficulties faced by ethnic outsiders attempting to make a living in Canada’s ‘multicultural mosaic.’ Although the text undeniably exposes the material and psychological challenges faced by immigrants, it also situates this human struggle in the context of a plurality of biological beings fighting for survival within the urban ecosystem of downtown Montreal. The text draws on romanticized notions of idyllic, natural beauty and subverts them to reveal the ideological shortcomings present in dominant discourses surrounding the representation of the environment and of human relationships with urban space. Instead of focusing on the aesthetic appeal of landscapes that appear wild and uncompromised by human disturbance, Hage forces the reader to gaze upon the city, its garbage and sewage, disrupting seemingly obvious divisions between food and waste in the narrative. Advancing a new mode of aesthetic appreciation for “contaminated” environments and their “edible” potential in the modern urban setting, the novel’s poetic language and its aesthetic sensibility function as rhetorical devices to envision a restructuring of common conceptions of human-nonhuman relations and a more ethical approach to consumption in an already compromised global ecosystem.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.343
Teacher spread0.314 · 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 designNot applicable
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
Published2014
Admission routes3
Has abstractyes

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