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Record W2326783374 · doi:10.1177/011719680000900403

An Examination of the Employment Structure of Taiwanese Immigrants in Australia

2000· article· en· W2326783374 on OpenAlexaboutno aff
Nora Chiang, Liangwen Kuo

Bibliographic record

VenueAsian and Pacific migration journal · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDestinationsDemographic economicsCensusEmigrationGeographySurvey data collectionEconomic growthPolitical scienceDemographyTourismSociologyPopulationEconomics

Abstract

fetched live from OpenAlex

Taiwanese emigration has reached a considerable level in the last ten years, with 20,0000 to 25,000 annually heading for mjaor destinations such as the United States, Canada, Australia, and New Zealand. Rapid economic growth, political instability, increased personal income, and relaxation of travel restrictions since 1989 are behind this increase. Particularly for Australia, the introduction of economic and business migration policies attracted skilled and entrepreneurial groups. This study examined the employment structure of the Taiwan-born in Sydney, Melbourne and Brisbane, the top destinations of Taiwanese immigrants in Australia. Unpublished census data for the years 1981, 1986, 1991 and 1996, supplemented by data from a survey of Taiwanese immigrants in the three cities, provided data for the study. Findings suggest that the employment rate of Taiwanese immigrants is among the lowest of all immigrant groups. Taiwanese men tend to be more economically successful than Taiwanese women in income levels, occupational status, and labor force participation. Most Taiwanese immigrants in Australia experience downward social mobility due to factors such as unaccredited overseas qualifications, the lack of English proficiency and local knowledge, and other institutional discriminatory factors.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations12
Published2000
Admission routes1
Has abstractyes

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