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Record W4236380681 · doi:10.24124/2012/bpgub833

Chinese immigrant women in remote communities: Adjustment and social support networks.

2012· dissertation· en· W4236380681 on OpenAlexaboutno aff
Yufen Hsiao

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationThematic analysisChinaMainland ChinaUnemploymentSocial supportMainlandNarrativeGeographyPolitical scienceGender studiesEconomic growthSociologyPsychologyQualitative researchSocial psychologySocial scienceEconomics

Abstract

fetched live from OpenAlex

This study used a combined method of narrative analysis and thematic analysis to explore the adjustment process for Chinese immigrant women in remote settings. Participants who were originally from Mainland China, but came to Canada under sponsorship or with their husbands were interviewed. Six themes and 19 sub-themes emerged from the data, which show that Chinese immigrant women in remote settings experience social isolation, unemployment, severe weather, a different lifestyle, marriages behind screens, and challenges of parenting. Some challenges occur regardless of the settings (urban or rural), whereas others happen or are exacerbated in remote communities. In remote communities of northern B.C., unique conditions and severe weather can result in difficulties of adjustment. Nonetheless, the women may have more job opportunities in remote settings even if these are low paying, manual labour jobs. The findings also demonstrate that the women's social support networks are one of the most important factors in adjusting to the new country. --P. i.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.013
GPT teacher head0.292
Teacher spread0.279 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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