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Record W4205232820 · doi:10.1080/10630732.2021.2004069

Of Flying Cars and Pandemic Urbanism: Splintering Urban Society in the Age of Covid-19

2022· article· en· W4205232820 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Urban Technology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork University
Fundersnot available
KeywordsUrbanismAnticipation (artificial intelligence)Coronavirus disease 2019 (COVID-19)MobilitiesPandemicHistoryPolitical scienceSociologyEconomic geographyPolitical economyGeographySocial scienceMedicineArchitectureComputer scienceDiseaseArchaeology

Abstract

fetched live from OpenAlex

Disappointingly to many who grew up at the time, promises of flying cars in the 1960s as a future form of urban transportation were not kept. That future never arrived. In this short commentary, I want to board the metaphorical flying car and steer it into a different direction. At the height of the first wave of Covid-19, a more widespread sentiment took hold that saw the anticipation of increased mobilities dashed by a general anticipation of disaster considered typical for our age today. We might conclude: We don't get the technologies we want because we have left the era of technological progress and entered the era of risk and anticipation of disaster. My commentary appreciates and discusses the lessons we can learn from Splintering Urbanism for our period of pandemic urbanism. How does the kind of networked urbanism that the book examines and critiques provide a framework in which we can understand the emergence, presence, and management of the pandemic as it affects our urban world today?

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)0.000
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 score0.680
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.335
Teacher spread0.296 · 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