Unmasking China’s Great Leap Forward and Great Famine (1958-1962) Through <i>Shunkouliu</i> (顺口溜)
Bibliographic record
Abstract
Satiric Shunkouliu (顺口溜), an oral folklore tradition among Chinese peasants known as “slippery jingles” or “doggerels,” express discontent and often contain disguised critiques of official propaganda. In this article, I call upon Shunkouliu to expose the reality behind the dogma during China’s Great Leap Forward and Great Famine (1958-1962). This departs from existing scholarship that has focused on written texts and interviews as primary data. Analyzing Shunkouliu demonstrates the collective efforts of Chinese peasants in speaking the truth. Through its satiric and disruptive qualities, Shunkouliu challenged official rhetoric by making erased realities visible and silenced voices audible. Recognizing Shunkouliu as legitimate data also challenges positivist criteria (representativeness and sample size) in assessing data credibility. I conclude this article by urging qualitative practitioners in the global South to explore forms of data beyond those traditionally examined within the parameters of qualitative research originating in the global North.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".