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Record W4412779050 · doi:10.5430/wjel.v16n1p217

Thainess as Part of Thai Diaspora Identity in Thai American Literature: A Case Study of Manning’s A Good True Thai and Lapcharoensap’s Sightseeing

2025· article· en· W4412779050 on OpenAlexvenueno aff
Kittiphong Praphan

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAsian American and Pacific Histories
Canadian institutionsnot available
Fundersnot available
KeywordsDiasporaIdentity (music)TourismSociologyGender studiesHistoryPhilosophyAestheticsArchaeology

Abstract

fetched live from OpenAlex

The study of Thai American literature is a new area of exploration in Asian American literature scholarship. This study investigates two literary works by Thai American authors, Sunisa Manning’s A Good True Thai and Rattawut Lapcharoensap’s Sightseeing, aiming to uncover significant traits of Thainess as a part of the Thai diaspora identity. Employing the framework molded by Asian American criticism and the concept of cultural identity, this study manifests that Thailand, the authors’ home country, is represented through different traits of Thainess as their cultural identity, constituting their diaspora identity. In the first book, the monarch is portrayed as a central trait of Thainess, suggesting that an ideal Thai citizen must revere and express loyalty to the kings who are depicted as the national saviors and unifying figures in times of crisis. On the other hand, Sightseeing represents Thainess in the form of tourist destinations and prostitution, a more common image deeply rooted in Western imagination, which is a result of the Vietnam War. The representation of Thainess in these two books asserts the Thai diaspora identity as an integral part of the authors’ Asian American identity. Presenting the background of the authors’ home country, these two literary works serve as a cultural connection between America and Thailand, aligning with the evolving trend of Asian American literature.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.058
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.300
Teacher spread0.293 · 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 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
Published2025
Admission routes1
Has abstractyes

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