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Record W4390033736 · doi:10.1080/00918369.2023.2295336

Job-Seeking Experiences of Trans Adults in South Korea

2023· article· en· W4390033736 on OpenAlexaff
Jimin Sung, Jaehee Yi, Min Ah Kim, Gaben Sanchez

Bibliographic record

VenueJournal of Homosexuality · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSnowball samplingPsychologyPsychosocialSocial psychologyIdentity (music)Psychological interventionOppressionQualitative researchJob attitudeHelp-seekingTransgenderCareer developmentJob satisfactionJob performanceSociologyMental healthPolitical scienceMedicinePolitics

Abstract

fetched live from OpenAlex

Being trans is stigmatized and can make it difficult to fit into the job market in South Korean society. This study explored trans individuals' job-seeking experience and the impact of gender identity on their career choices and development using a qualitative approach. In-depth interviews were conducted with 20 trans adults with job-seeking experiences who were recruited through purposive and snowball sampling in South Korea. Ten subthemes were identified in three overarching themes: (a) limiting myself in job search; (b) challenges in the job application and interview process; and (c) having a desire to build a meaningful career. Participants limited their choices for employment in favor of gender-neutral jobs or trans-inclusive work environments. In the job-seeking process, they faced challenges due to society's rigid binary gender roles and the negative stereotypes about trans identities. Despite stress and identity-related conflict, participants expressed a desire to overcome challenges, build a meaningful career, and flourish at work without compromising their gender identity. This study highlights the experiences of trans individuals in their job-seeking journey. Psychosocial interventions and career support services could help trans individuals in the job-seeking process by identifying their unique challenges to employment and providing assistance to cope with stigma and oppression.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.406
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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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