<i>Sparks: China’s Underground Historians and Their Battle for the Future</i> by Ian Johnson
Bibliographic record
Abstract
This book is a series of profiles of grassroots historians in China whose work documents the atrocities and traumas of Communist Party rule, including land reform, the Anti-Rightist Movement, Great Leap Famine, and violence of the Cultural Revolution, all the way up to the outbreak of covid-19 in Wuhan. Johnson, a longtime foreign correspondent for the New York Times in Beijing until he was expelled from China in 2020, sees these underground historians as a resistance movement poised to spark broader opposition to the Communist Party in moments of crisis.It is a rare and welcome experience for me to read a book about Chinese history with which I want to spend time and continue learning from the main characters, as well as the author himself. Johnson accomplishes this sense of camaraderie by inserting himself into the narrative as a sympathetic supporter of the remarkable individuals that he profiles. He shares their sense of grievance and righteousness as he travels with them to such “places of memory” as the Jiabiangou forced labor camp, where thousands of people labeled as “rightists” starved to death in the early 1960s, and to Daoxian County in Hunan Province, where author Tan Hecheng introduced Johnson to survivors of a massacre that targeted entire families of “class enemies” during the late 1960s.By putting human stories at the center of the book—the stories of victims whose deaths have been covered up and the stories of editors, writers, and filmmakers who expose the cover-ups at great personal risk—Johnson succeeds in giving credit to the pioneering work of grassroots historians while broadening awareness of the brutalities that they are obsessed with uncovering. The book is an indictment of a political system that attempts to control histories and memories that run counter to the Communist Party’s official triumphalist narrative and that punishes and threatens those who dare to research and publish their findings independently.All of this sounds quite dire and depressing. Although Johnson acknowledges that space for underground historical work has been shrinking steadily, especially since Xi Jinping came to power in 2012, he remains optimistic. The resilience of grassroots history, Johnson argues, reveals the limits of state control and has been greatly aided by technology. Anyone with a phone, camera, or computer can quickly share documents and videos, which create and broaden communities of like-minded critics. Because it is so risky to challenge the system and amplify unofficial memories in China today, most people stay silent. But as Johnson shows, in such moments of crisis and uncertainty as the protests against “zero covid” lockdowns in 2022, the work of underground historians finds larger audiences who reflect on how censorship and other problems of authoritarian rule affect their own lives.One potential critique of Johnson’s book is that it is too derivative in the way it describes and synthesizes Chinese-language research (some of which has already been translated into English), sharing the findings of the late Gao Hua on the Yanan Rectification Movement, Jiang Xue on rightists’ suffering in Jiabiangou, and Tan Hecheng on massacres in Hunan, among many others. Readers interested in these specific topics could simply learn from these historians’ original works. Readers who want academic analysis of underground history can find it in Sebastian Veg’s excellent Minjian: The Rise of China’s Grassroots Intellectuals (2019).This line of criticism is to be expected from experts who are already familiar with the topics and people that Johnson investigates, but it is not entirely fair. Johnson has deployed a big megaphone to bring attention to people who are unable to operate openly within Chinese academia but who are committed to exposing injustice and promoting their versions of truth. Unlike any other book, Johnson takes readers inside the work of doing underground history, giving them a sense of the sounds, sights, smells, fears, tears, friendships, and laughter that connect victims and their chroniclers. He also allows readers to discern patterns that emerge from his subjects’ stories, most notably the phenomenon of critical, independent thinking being cultivated inside family homes across multiple generations rather than in China’s school system. Perhaps Johnson’s greatest achievement is that he himself has become an underground historian trusted by his subjects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".