MétaCan
Menu
Back to cohort
Record W3014374347 · doi:10.5539/ass.v16n4p87

A Study on Identity of New Chinese Immigrants in Bangkok

2020· article· en· W3014374347 on OpenAlexvenueno aff
Jianghua Han

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesSichuan University
KeywordsChinaSettlement (finance)Identity (music)ImmigrationEthnic groupCultural identityChinese cultureGeographySocioeconomicsGender studiesSociologySocial scienceBusinessAnthropology

Abstract

fetched live from OpenAlex

Survey results of this study have showed that: The Chinese new immigrants in Bangkok have a consistency on ethnic identity; they all agree that they are Chinese. However, on the national identity and cultural identity, the identity of them has multi-tendency. There are 45.29% respondents identify China, 30.63% respondents identify Thailand, and 24.08% respondents identify both China and Thailand. The degree of identifying China of them has declined with the increase of their settlement years in Bangkok; however, their degree of identifying Thailand has increased with the increase of settlement years in Bangkok. The cultural identity is very complicated, they are increasingly accepting and identifying Thai culture with the increase of their settlement years in Bangkok; however, they did not deny or abandon Chinese culture, lots of people still identify Chinese culture. Especially in the identity of traditional culture, in general, the degree of identifying Chinese traditional culture of them has declined with the increase of their settlement years in Bangkok; however, the proportion of people who identify Chinese traditional culture is still much higher than people who identify Thai traditional culture.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.449
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.382
Teacher spread0.337 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicSoutheast Asian Sociopolitical StudiesFrench-language works237,207