Bibliographic record
Abstract
This book offers an illuminating exploration into the complex world of political communication in South Korea from 2016 to 2021. Through an in-depth analysis of 34 political conversations totalling over 275 hours, this book presents a groundbreaking interdisciplinary study combining quantitative and qualitative methods. It delves into the intricate design and strategic use of questions and answers in political dialogue, shedding light on the underlying rhetoric, strategy, and power dynamics. By examining the seismic shifts in South Korea's political landscape, including a major political scandal, the impeachment of the president, North–South relations, and the COVID-19 pandemic, this work presents a unique perspective on how political conversations shape, and are shaped by, societal and global events. It is a vital contribution to the study of Korean linguistics, offering tools and frameworks for analyzing political dialogue in a political setting. An indispensable resource for scholars and students in the fields of linguistics, political science, communication studies, and Asian studies, as well as political enthusiasts and professionals engaged in diplomatic and governmental sectors. It offers readers insights into the nuanced strategies of political discourse, enhancing their understanding of how language shapes politics and vice versa.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".