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Record W2739647987 · doi:10.32920/ryerson.14648106.v1

The resettlement experiences in Canada of Chinese-Vietnamese refugees after the Vietnam war

2021· preprint· en· W2739647987 on OpenAlexaffabout
Belinda Ha

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCarleton UniversityToronto Metropolitan University
Fundersnot available
KeywordsVietnameseRefugeeMindsetUnderemploymentSocial capitalGovernment (linguistics)Vietnam WarPolitical scienceEconomic growthSociologyGender studiesPsychologySocial psychologyDevelopment economicsUnemploymentSocial scienceEconomics

Abstract

fetched live from OpenAlex

Through six intensive and semi-structured interviews, this research paper examines the role social support networks may or may not have played in facilitating the resettlement experiences of Chinese-Vietnamese refugees living in Canada after the Vietnam War. It was observed that those who were privately-sponsored emphasized the instrumental role their benefactors played in assisting their successful resettlement, whereas those who were government-sponsored were forced to adopt a more independent mindset of forming their own social support systems. Regardless of sponsorship type, the notion of hard work was a commonality found amongst the participants. They arrived at the receiving country expecting and willing to accept conditions of underemployment and downward mobility. The effects of pre-migration enabling factors such as marital status and educational attainment are also acknowledged. Within a social support framework, theories of social capital and resiliency are used to analyze the lives of the participants after the traumatic experience of forced migration.

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)0.004
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 score0.232
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.007
Scholarly communication0.0030.001
Open science0.0010.004
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 head0.008
GPT teacher head0.289
Teacher spread0.281 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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