MétaCan
Menu
Back to cohort
Record W1983440011 · doi:10.1080/01459740.2010.501314

Feminine Transformations: Gender Reassignment Surgical Tourism in Thailand

2010· article· en· W1983440011 on OpenAlexaboutno aff
Aren Z. Aizura

Bibliographic record

VenueMedical Anthropology · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTourismGender studiesSociologyMedicineGeography

Abstract

fetched live from OpenAlex

Every year, hundreds of transgendered people from the United States, Europe, Asia, Canada, and Australia travel to Thailand to undergo cosmetic and gender reassignment surgeries (GRS). Many GRS clinics market themselves almost exclusively to non-Thai trans women (people assigned a male sex at birth who later identify as female). This article draws on ethnographic research with patients visiting Thailand for GRS to explore how trans women patients related their experience of medical care in Thailand to Thai cultural traditions, in particular "traditional" Thai femininity and Theravada Buddhist rituals and beliefs. Foreign patients in Thai hospital settings engage not only with medical practices but also with their perceptions of Thai cultural traditions--which inflect their feminine identifications. I draw on two patients' accounts of creating personal rituals to mark their gender reassignment surgery, placing these accounts within the context of biomedical globalization and debates about the touristic appropriation of non-"Western" cultural practices.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.009
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.050
GPT teacher head0.450
Teacher spread0.400 · 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

Citations76
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMedical AnthropologySame topicGlobal Healthcare and Medical TourismFrench-language works237,207