MétaCan
Menu
Back to cohort
Record W4415335011 · doi:10.1007/978-3-032-00423-9_13

Culture in the Global Urban Margins: Cultural Policymaking with Migrant Workers in Doha and Singapore

2025· book-chapter· en· W4415335011 on OpenAlexaff
Jérémie Molho

Bibliographic record

VenueIMISCOE research series · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCognitive reframingDilemmaNarrativeFace (sociological concept)PrecarityTransformative learningDiversity (politics)Cultural diversityMigrant workers

Abstract

fetched live from OpenAlex

Abstract This chapter examines the evolving role of cultural policy in the urban integration of migrants. It compares Doha and Singapore, cities with large populations of transient, low-wage labor migrants lacking permanent status. The study, based on fieldwork and interviews conducted from 2018 to 2022, analyzes cultural actions that have been deployed towards these populations in recent years, ranging from the building of specific cultural infrastructures, like movie theaters showcasing Bollywood films, to the organization of photography festivals and poetry contests. This study identifies three distinct policy approaches: one perpetuating exclusion, another reinforcing the prevailing diversity management regime, and a third aiming to reframe the migrant discourse and advocate for transformative change. The chapter puts the spotlight on the ’cultural city-makers’ who are at the forefront of such cultural policies, often migrants themselves, with a status that oscillates between different forms of precarity and privilege. I explore the dilemma that they face as they realize these cultural initiatives with these marginalized migrants, such as the risk of legitimizing unjust and exploitative policies, and the personal risks that their attempts to challenge dominant narratives could generate for themselves.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.799
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
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 head0.069
GPT teacher head0.405
Teacher spread0.336 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIMISCOE research seriesSame topicSocioeconomic Development in AsiaFrench-language works237,207