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Record W3129978109 · doi:10.5539/hes.v11n1p171

Learning Management on Sexual Diversity in Social Studies through a Case Study on Identity Formation in LGBT Elderly

2021· article· en· W3129978109 on OpenAlexvenueno aff
Nipitpon Nanthawong, Thongchai Phuwanatwichit, Charin Mangkhang, Atchara Sarobol

Bibliographic record

VenueHigher Education Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLesbianHuman sexualityTransgenderDiversity (politics)Sexual identityPsychologyIdentity (music)Gender studiesFocus groupHomosexualitySexual orientationQualitative researchSociologySocial psychologySocial science

Abstract

fetched live from OpenAlex

The purpose of this research is to study learning management on sexual diversity in social studies through a case study on identity formation in the LGBT elderly. The sample included 12 LGBT (Lesbian, Gay, Bisexual, and Transgender) elderly people determined by the concept of age ranges or generations. This study is in the form of a qualitative study by using the methodology, autobiography, and storytelling of life history. The results of the study revealed that these LGBT elderly people developed or formed LGBT identity at their early age before entering to acceptance of LGBT. Most of them were aware that they had a sexual identity different from general people since they were young. Some of them accepted such identity and express it right away whereas some tried to hide it since it was unacceptable in their living societies. Then they sought knowledge by themselves through direct experience and from other people with the same sexuality. However, these LGBT people thought that education should take the role to educate about LGBT to understand LGBT people as well as others. Regarding learning management, the focus should be on the target group of teenage students by emphasizing understanding and awareness of right, liberty, and equality in genders and societies.

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 score0.059
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.261
GPT teacher head0.518
Teacher spread0.257 · 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
Published2021
Admission routes1
Has abstractyes

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