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Record W4405571965 · doi:10.33063/diva-544570

Gatekeeping fare collection in late industrial urbanity: Infrastructural labour in the gate milieu of the Stockholm metro

2024· article· en· W4405571965 on OpenAlexfundno aff
Jenny Lindblad

Bibliographic record

Venuekritisk etnografi Swedish Journal of Anthropology · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
FundersYork University
KeywordsUrbanityGatekeepingBusinessEngineeringAdvertisingCivil engineering

Abstract

fetched live from OpenAlex

Metro stations around the world are equipped with gate infrastructure to collect fares from passengers. At the Stockholm metro, the regional authority replaced tripod turnstiles with electronic gates aiming to better prevent fare evasion and increase revenue following reduced public transport subsidies. In this article, I engage with the politics of fare collection by attending to the gate environment in metro stations as constituting a milieu designed to regulate circulation. Rather than examining the gate milieu through its upfront purpose of fare collection, I critically examine the urban political relations generated and foreclosed in encounters with the material and semiotic properties of the gates. The margin of indeterminacy presented as the gates’ doors slide open upon a ticket validation, invites passengers to assist the gates, in either blocking or letting pass the following passenger to get through. Together with the regional authority’s framing of fare evasion as a cause for a degrading public transport infrastructure, the gate milieu pulls passengers into performing the work of fare collection. As such, the gate arrangement individualises responsibility among passengers for the maintenance of the metro as a collective good. Ultimately, the gate arrangements and the moralized repertoire in which they are inscribed, reveal how fare collection infrastructure risks contributing to escalating urban injustices in times of late industrial urbanity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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