The Communist hero and the April Fool's joke: the cultural politics of authentication and fakery
Bibliographic record
Abstract
In 1963, Chairman Mao made a national hero of an ordinary soldier named Lei Feng, said to have been inspired by collectivism to do countless selfless deeds. Sceptical observers inside and outside China disparage the persistent Lei Feng legend, judging it to be a laughably fraudulent construct of the Communist Party. We take this contrast as an opportunity to examine the cultural politics of authentication and fakery. We show that critics of the Chinese regime take the propagation of ‘inauthentic’ evidence to be indicative of a government based on illegitimate tactics, or of a credulous population. Meanwhile, research participants in China, who consider Lei Feng stories and artefacts to intermingle evidential and pedagogical representation, justify the state's curation of the legend for societal good. Second, we contrast Chinese and western discussions about a Lei Feng‐related trick that a western news agency is said to have played on China on April Fool's Day. Examining the different framings of this story – that of satire, practical joke, fake news and rumour – we argue that how the fake is decoded depends on what political ends it serves, which include the ideological legacy of the Cold War as well as contemporary USA–China competition.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".