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Record W6983311786

Married to Singapore : what are migrant wives saying 'I do' to?

2021· other· en· W6983311786 on OpenAlexaboutno aff

Bibliographic record

VenueDR-NTU (Nanyang Technological University) · 2021
Typeother
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationFace (sociological concept)Quarter (Canadian coin)Migrant workersDomestic violencePandemic
DOInot available

Abstract

fetched live from OpenAlex

Migrant wives are a significant community in Singapore, where nuptials between migrant women and Singaporean men contribute to a quarter of all citizen marriages. Despite being wives and mothers to Singaporeans, migrant wives are still sidelined when it comes to immigration policies, social benefits, and employment opportunities. \n \nComing from developing nations in Asia, many enter Singapore with little social ties and a lack of knowledge about their rights, leaving them more susceptible to family violence than local wives. Last year, the pandemic had brought to light the severity of family violence cases in Singapore, but the specific issues that abused migrant wives face are still shrouded in darkness. Despite the challenges, some individuals and groups refuse to give up lobbying for these women’s rights. \n \nThis feature explores the challenges that these women have to overcome, the Singaporeans who fight alongside them, and the complexities that come with marrying into Singapore.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.040
GPT teacher head0.293
Teacher spread0.253 · 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
Published2021
Admission routes1
Has abstractyes

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