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Record W3106923209 · doi:10.22148/001c.18120

A Computational Approach to Urban Space in Science Fiction

2020· article· en· W3106923209 on OpenAlex
Federica Bologna

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Cultural Analytics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSpace (punctuation)Techno-thrillerFiction theoryPoint (geometry)Urban spaceUrban planningLiterary fictionGeographyComputer scienceRegional scienceMathematicsLiteratureArtLiterary criticismEngineeringCivil engineering

Abstract

fetched live from OpenAlex

This study analyzes the presence of urban space in 20th century science fiction in English using computational methods. Three theoretical approaches are used to model urban space as a measurable feature. First, urban space is formalized as a topic. LDA topic modeling is used to retrieve the urban topic from the corpus and estimate its presence in each book. Secondly, urban space is formalized as the sum of the linguistic fragments that form a setting. A list of urban terms is created and their frequency is measured for each novel. Lastly, cityspace is formalized as the number of references to urban locations. Textual Geographies’ geographic data was used to measure the presence of named urban locations in each book. The results of these approaches all point to similar conclusions. A low presence of urban space is found in science fiction compared to general fiction, alongside a historical trend. Urban presence in science fiction is greater at the beginning of the 20th century, declines in the 30s and 40s, and successively increases in the 50s. No such dip is present in other types of fiction across the twentieth century.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.047
GPT teacher head0.323
Teacher spread0.276 · 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