MétaCan
Menu
Back to cohort
Record W3193169778 · doi:10.1177/00420980211031721

‘Timepass’ and ‘setting’: The meanings, relationships and politics of urban informal work in Delhi

2021· article· en· W3193169778 on OpenAlexaff
Sanjeev Routray

Bibliographic record

VenueUrban Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNegotiationSociologyPoliticsInformal sectorEveryday lifeState (computer science)Work (physics)HegemonyEthnographyCommissionReproductionWage labourPolitical economyEconomic growthPolitical scienceEconomicsSocial scienceLawGeography

Abstract

fetched live from OpenAlex

The engagement of people in extremely low-wage work in major cities of the Global South and their withdrawal from labour-organising activities arise from several factors. Among these is the hegemonic meaning construction of work as ‘timepass’ or leisure and as an opportunity for sociality and neighbourliness that is central to the social reproduction of everyday life. Drawing on ethnographic fieldwork in Delhi, this article examines how work regimes are marked by a ‘commission economy’, whereby various stakeholders in the chain of surplus accumulation demand a commission for their services. The possibility of undertaking informal economic activities is contingent on a host of improvisations that are founded upon discipline, violence and also solidarity. In this respect, various stakeholders possess what they refer to as a ‘setting’, which alludes to an active process of economic and non-economic relationship-building with both state and non-state agencies within both formal and informal arenas. To ‘do setting’ is a dynamic spatial process that draws on negotiations with the aim of shaping favourable relationships and outcomes in particular urban spaces. It entails the use of social and cultural resources, everyday political negotiations and extra-judicial solutions.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.287
Teacher spread0.244 · 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 designObservational
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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueUrban StudiesSame topicUrban Planning and GovernanceFrench-language works237,207