Bibliographic record
Abstract
Years ago, I had an argument about the possibilities of doing North Korean history.I was less than optimistic: too much Kim Il Sung, too much propaganda, not enough sources-or so I thought.In this book, I try to prove myself wrong.Along the way, I owe a debt to a succession of motivated students.They pestered me about North Korea because they knew enough not to believe the caricatures in our media.Textbooks on Korean history in my own student days simply stopped talking about the North after the civil war-half the peninsula erased.My own studies provided me with no better answers.I preferred simply to avoid the topic altogether, as it is always easier to critique than to offer alternatives.My students were unfazed by my frowns, however.Their irksome questions kept coming.This book is, ultimately, a cantankerous teacher's attempt to make amends and offer something of an answer, however belated, to those pesky yet valuable queries.Along the way, I've received much prodding from a wide array of colleagues and scholars, who have made this work possible and much richer.All the shortcomings remain my own.I have the good fortune to study at a university devoted to thinking globally about East Asia, the University of Toronto.I'm grateful to my colleagues who have presented formal seminar critiques of chapters of this work-Yi Gu and Lisa Yoneyama-and to others who have taken the time to read and discuss specific issues and comparative approaches, including
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.365 | 0.236 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".