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Record W3083370477 · doi:10.4000/civilisations.5542

Othering mechanisms and multiple positionings: Children of Thai-Belgian couples as viewed in Thailand and Belgium

2019· article· en· W3083370477 on OpenAlexaboutno aff
Asuncion Fresnoza‐Flot

Bibliographic record

VenueCivilisations · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersFonds De La Recherche Scientifique - FNRSUniversity of Brighton
KeywordsNationalityEthnic groupViewpointsCitizenshipGender studiesSociologyPopulationFocus groupImmigrationGeographyPolitical scienceDemography

Abstract

fetched live from OpenAlex

Studies on the so-called “second generation” mainly focus on individuals whose parents are both migrants. This overlooks the situation of the children of “mixed” couples, in which one parent is a migrant and the other a citizen of the country in which they live. These mixed-parentage young people mostly inhabit cross-border social spaces that connect their parents’ respective countries of origin. Given this situation, how are these young people viewed in the countries in which they are enmeshed? How do they position themselves in relation to the viewpoints and stereotypes about them in these social spaces that traverse the borders of nation-states? To answer these questions, the present study examines the case of children of Thai-Belgian couples, who are called luk-kreung (half-child) in Thailand and métis in Belgium. Analysis of the empirical data gathered using qualitative methods shows that the study informants had access to citizenship in both of their parents’ countries of origin. Nonetheless, they remained widely subjected to othering due to their phenotypic characteristics, which are perceived as “different” from those of the majority population. This othering prompted them to adopt multiple positioning strategies: invisibilising their ethnic roots, accepting and highlighting their supposed “Otherness”, and acquiring Thai nationality (for those who did not yet have it).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.007
GPT teacher head0.242
Teacher spread0.236 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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