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Record W4413406524 · doi:10.1111/oli.70009

Supplementing, restructuring, resisting: Maps of Underground space in poetry, embodied performativity, and the “misrepresentationalism” of Harry Beck's Tube diagram

2025· article· en· W4413406524 on OpenAlexaff
Craig Melhoff

Bibliographic record

VenueOrbis Litterarum · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPerformativityEmbodied cognitionPoetryRestructuringDiagramSpace (punctuation)Tube (container)LiteratureArtAestheticsArt historyPhilosophyEpistemologyLawComputer scienceLinguisticsEngineeringPolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This article considers mental and poetic “maps” of London in their respective relationships to Harry Beck's famous 1930s “circuit‐diagram” map of the underground railway system. This iconic image distorts and radically stylizes London geography; thus, it functions as a tool for planning individual travel itineraries but leads to a misrepresentative top‐down mental map of the city. I focus on poetry by Michael Donaghy, D. J. Enright, and Carole Satyamurti that confronts the “ocularcentrism” of visual mapping and the stylization of Beck's image and, I argue, proposes the embodied experience of transit passengers as an alternative basis for conceptually organizing city space. These poets “write in” what pictorial cartography excludes, reorganizing London geography within the passenger's cognitive map and challenging the authority of Beck's image to establish what constitutes “official” territory via inclusion in or exclusion from the standard visual representation of the transit system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.029
Scholarly communication0.0060.005
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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