The Political Economy of the Chinese Food Traceability System: Cultivating Trust, or Constructing a Technocratic Certainty Machine?
Bibliographic record
Abstract
The World Health Organization (WHO) estimates that food contamination makes nearly 10% of the world population sick in 2020.Persistent food fraud also costs the global food industry billions of dollars every year (Reuters, 2020, para.61).More than 50,000 Chinese citizens got sick or died from numerous recent food safety incidents, such as rotting "pigwash" food in the high school canteen, stinking pork turned into packaged oil, and deceased pigs with disease re-entering the market and ending up on the Chinese citizen's table (BBC, 2019;Wang et al., 2019).Not only have these incidents stirred up public rage and outcry, but they have also undermined the public's trust in the food safety system (Kendall et al., 2019).Furthermore, in contrast to local supply, the global food supply chain no longer presents as stable and reliable due to economic, environmental, and political disruptions brought about by the e-commerce law (2019), the Covid-19 pandemic, African swine fever, and the ongoing trade war between the US and China.The sheer scale and rapid spread of Internet food-related rumours have also spurred the Chinese government to commit to boosting local food production, bolstering public trust, and collaborating with "dragon head" technology corporations to build a large-scale, highly automated, market-driven, smart and technocratic food traceability system.Understanding the food traceability system also has much broader ramifications because it has become the prototype for how other public policy issues are approached.For example, food traceability apps were quickly repurposed and adopted by the Chinese government as the basis of a mandatory COVID-19 health app distributed to all Chinese citizens.The upshot of these observations is that the rapid development of the food traceability system in China over the last decade or so is a microcosm for understanding much broader processes of social, economic, political and technological development in China.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".