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Record W4392502415 · doi:10.4087/pjcx8077

Intermarried Couples: Transnationalism, and Racialized Experiences in Denmark and Canada

2020· article· en· W4392502415 on OpenAlexaffabout
Rashmi Rashmi, Hema Ganapathy‐Coleman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransnationalismComputer scienceSociologyGender studiesPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Despite an increase in interracial or mixed marriages (intermarriages) globally, the experiences of couples in such marriages are generally under-researched, particularly within psychology. Using a cultural psychological framework and qualitative methods, this paper studies the psychosocial experiences of couples in intermarriages. It focuses on four South Asians in ethnically intermarriages in two settings: two Indian-origin men married to native Danish women in Denmark, and two Indian-origin women married to Euro-American men in Canada. Data from in-depth interviews were subjected to a thematic analysis yielding an array of themes, of which this paper presents the two most dominant themes across the two contexts: ‘transnationalism’ and ‘racialized experiences in social situations’. The results demonstrate that the participants lived transnational lives to varying degrees depending on their gender, socio-economic status and age, which in turn intersected with variables such as the nature of the transnational relationships they were attempting to sustain, and their own motivations and agency in maintaining these ties. While in some cases participants maintained a high level of contact with India through visits and digital technology, others kept up limited ongoing contact with the country of origin. Furthermore, varying racialized experiences emerged from the narratives, with differences in how these experiences were interpreted. While some participants recognized them as racial discrimination, others chose to rationalize these experiences in various ways. After offering an account of these results, the paper reflects briefly on the implications of these findings.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.086
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0300.006
Scholarly communication0.0050.001
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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
Published2020
Admission routes2
Has abstractyes

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