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Record W2316143304 · doi:10.1177/0096144215576716

City Glow

2015· article· en· W2316143304 on OpenAlexaffabout
Nicolas Kenny

Bibliographic record

VenueJournal of Urban History · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPrideConsumerismScholarshipFeelingPrestigePoliticsAngerPeriod (music)SociologyUrban environmentPolitical scienceAestheticsSocial psychologyPsychologyGeographyLawArt

Abstract

fetched live from OpenAlex

Proliferating streetlights generated complex emotional responses in modern cities. Drawing on recent scholarship in the history of the emotions, this article argues that examining the feelings of pride and prestige associated with technological innovation, but also of anger and fear when light was lacking or unpleasant, reveals the intimate nature of urban dwellers’ relationship to their environment. Street lighting is often studied as part the networks of infrastructure that gave cities their contemporary form, or as elements of the commercial expansion that made them centers of consumerism. At the intersection of these trends stood the emotional experiences of those seeking to lay claim to the urban night. If the cultural significance of emotions varies according to historical circumstances, comparing the tensions, politics, and atmospheres of streetlights in distant places like Montreal and Brussels suggests that the rapidly changing urban environment of the period produced its own distinct emotional regime.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.138
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1380.019

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 head0.099
GPT teacher head0.210
Teacher spread0.110 · 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
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

Citations7
Published2015
Admission routes2
Has abstractyes

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