Korean General Social Survey (KGSS): Cumulative File, 2003-2016
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.045 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".