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Record W3034989491

Street-Level Writing: Los Angeles in the Works of Charles Bukowski

2020· article· en· W3034989491 on OpenAlexaff
Marc Brosseau

Bibliographic record

VenueLiterary Geographies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsThe ImaginaryRepresentation (politics)InsiderContext (archaeology)Interpretation (philosophy)Perspective (graphical)SociologyFace (sociological concept)Point (geometry)AestheticsOrder (exchange)HistoryEpistemologyLinguisticsArtVisual artsPoliticsPsychologyPsychoanalysisPhilosophyLawSocial sciencePolitical scienceArchaeologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Los Angeles is central to Charles Bukowski’s (1920-1994) life and work. This paper examines the evolution of the representation of the city in his writings. It does so by situating the author in the broader context of the literary representation of Los Angeles in order to illustrate his particular perspective on the city and its many sites: that of a marginalized insider writing at street-level. It then proposes to reconstitute the dual trajectory of the author and his works in relation to the city. Interpreting Bukowski’s urban imaginary proves challenging because places are poorly fleshed out through description. Yet when approached as a whole, his writings do possess a coherent spatiality. Acutely myopic at first, the representation of the city gradually becomes more complex sociologically and geographically. Incomplete and patchy, the image of the city and its contrasted social worlds progressively acquires texture and depth. The paper finally argues the geographical imaginary Bukowski developed through his experience in the city’s underbelly informed his interpretation of L.A.’s social reality as a whole, provided him with the language and themes to express it, and supplied his very own vantage point to make sense of it all.

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 score0.524
Threshold uncertainty score0.391

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 studies0.0000.000
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 head0.062
GPT teacher head0.246
Teacher spread0.184 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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