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Record W6928888976 · doi:10.3886/icpsr37214

Korean General Social Survey (KGSS): Cumulative File, 2003-2016

2019· dataset· en· W6928888976 on OpenAlexaboutno aff

Bibliographic record

VenueICPSR Data Holdings · 2019
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral Social SurveyPoliticsSurvey data collectionGovernment (linguistics)ChinaEuropean Social SurveySurvey researchQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

The Korean General Social Survey (KGSS) is the Korean version of the General Social Survey (GSS), closely replicating the original GSS of the National Opinion Research Center at the University of Chicago. The KGSS comprises four parts: The first part includes replicating core questions that cover the core content of Korean society. The second part is the International Social Survey Program (ISSP) module, which is a cross-national survey of 45 countries from all over the world. The third part is the East Asian Social Survey (EASS) module. The EASS is a joint survey of four East Asian countries (Korea, Japan, China and Taiwan) conducting a GSS-type social survey. The last part contains modules proposed by researchers. This data collection is the cumulative version of the previous 13 years of survey data from 2003 to 2016 (not including 2015). Respondents were asked for their opinions about Korean society, economic conditions, government performance, politics and political conditions. Additional questions were asked regarding the health care system, respondents' health behaviors, human rights, attitudes toward aging and the elderly, household composition, household income, education, occupation, environmental issues, international migration and so on. Demographic information collected includes age, sex, education level, household income, employment status, religious preference, political party affiliation, and political philosophy.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.003
Threshold uncertainty score1.000

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 science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.330
Teacher spread0.263 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes1
Has abstractyes

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