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Record W2524809171 · doi:10.11575/prism/27010

Mosaic of Spaces: Social Integration of Chinese Immigrant Women in Toronto

2015· dissertation· en· W2524809171 on OpenAlexaboutno aff
Bonnie Lee

Bibliographic record

VenuePRISM (University of Calgary) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsMosaicImmigrationGender studiesSociologyGeographyPolitical scienceMedia studiesArchaeology

Abstract

fetched live from OpenAlex

This study investigates how Chinese immigrant women subjectively experience their social integration in Canada and the way it is constituted socially and symbolically, based on a secondary qualitative data analysis. Spatial and social constructionist theories inform the analysis of five in-depth interviews with Chinese immigrant women in Toronto. Social integration is a process built through a myriad of social interactions in a gender-related mosaic of spaces. An initial typology of spaces is thematically characterized by its cast of actors, power relations and differences in social, cultural and multiple forms of capital. The functions of each space in increasing the immigrants’ set of capital to become participants and contributors in Canadian society are illustrated. Viewing physical and social spaces as mutable with creative potential, implications of a spatial typology in facilitating immigrant social integration are discussed in terms of social work theory, practice, and education.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0170.008
Scholarly communication0.0040.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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