Food Safety in China: The Structure and Substantive Foci of an Emerging Field of Social Science Research
Bibliographic record
Abstract
This paper is the first to describe the structure and content of the English language social science literature on food safety in China. To do this research we systematically searched Web of Science and Scopus, the most comprehensive indexes, using the terms “Food Safety” AND “China” OR “Chinese”. To focus our search results, we used the index features available on Web of Science and Scopus, and limited results to the English language, peer-reviewed journal articles, social sciences, and published in the period of 2009 to 2015. This resulted in 272 selected journal articles, with a final data set of 185 articles for review. A food safety system model we developed was used to classify and present the findings derived from content analysis of abstracts, titles, and keywords. Our findings show that the research reviewed is unevenly distributed across the components of the food safety system model. The greatest proportions of the literature reviewed focused on consumers, primary and secondary producers and products, and government legislators and regulators, respectively. Smaller proportions focused on food wholesalers, retailers, researchers, educators, and the media. Few of the articles reviewed used a model of the food safety system. None identified an explicit knowledge transfer strategy.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.052 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".