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Migration, Gender, Cities

2019· other· en· W2940305191 on OpenAlexaff
Rachel Silvey, Symon James‐Wilson, Tamir Arviv

Bibliographic record

VenueThe Wiley Blackwell Encyclopedia of Urban and Regional Studies · 2019
Typeother
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGentrificationCitizenshipScholarshipSociologyGender studiesInequalityPoliticsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

Abstract In recent decades, scholarship in the social sciences has increasingly examined the role of gender in migration, the dynamics connecting migrants with cities, and the ways that intersections of race, class, and gender inform migrants' struggles over citizenship. Global cities have relied on low income gendered and racialized labor migrants to fulfill precarious service sector jobs, and urban growth has coincided with a series of sociospatial dislocations associated with neoliberalization, securitization, gentrification, segregation, and income polarization. Feminist and critical race scholars of urban studies emphasize the coconstructed nature of urban sociospatial inclusion and exclusion, and understand migration as central to the (re)production of difference and inequality in cities and globally. Research has attended to the politics of identity and scale, and in so doing has increasingly considered new geographies of belonging, occupancy, and citizenship within and beyond the nation‐state.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0470.003

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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
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
Published2019
Admission routes1
Has abstractyes

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