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Record W3083379431 · doi:10.1177/1461444820913567

Temporal arbitrage, fragmented rush, and opportunistic behaviors: The labor politics of time in the platform economy

2020· article· en· W3083379431 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

VenueNew Media & Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTemporalityArbitragePoliticsOrder (exchange)EthnographySociologyService (business)EconomicsEconomyPolitical scienceLawEpistemologyFinancial economics

Abstract

fetched live from OpenAlex

This article examines how on-demand service workers on digital platforms make and live their time in the case of China’s food delivery industry. Using ethnographic data, the study elucidated multiple facets of couriers’ temporality in their struggle to meet the exacting delivery time imposed by platforms while moving through biased urban spaces as marginalized temporal subjects. It is argued that a new temporal order, referred to as temporal arbitrage in this study, has been normalized in the recent platform economy. It shifts the customer’s cultural expectation to on-demand service at the expense of an increasingly hectic tempo for the workers. We demonstrate the mundane, and sometimes opportunistic, tactics deployed by workers to reconstruct their temporality. The article connects the workers’ temporality to the urban spaces, digital work process, and socioeconomic structures. It fills an important research gap by addressing the under-explored yet essential temporal dimensions in the expanding “just-in-time” labor force.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.261
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