Synecdochic and metonymic chains in the organizing narratives of PRC forensic genetic research targeting Uyghurs in Xinjiang
Bibliographic record
Abstract
In this paper, I demonstrate how synecdochical and metonymic chains function in the semiotic narrative schema of forensic genetics articles. Greimas’s narrative schema and work on categories provides a framework that can be synthesized with cognitive linguistic advances on synecdoche and metonymy chains to understand how meaning is created in organizational interactions through the circulation of objects of value. Based on an empirical analysis of controversial forensic genetic articles involving Uyghur subjects on ancestry inference marker and phenotype (visible appearance) inference marker research projects, it shows how scientists from the Chinese Ministry of Public Security and other scientists organize through a shared manhunting narrative schema. In this schema, Society and/or Humanity send scientists quests to protect society by improving forensic genetic technologies to variously track down and capture or kill criminals, separatists, insurgents and terrorists. Utilizing recent advances in theorization of synecdoche, the article shows how synecdoche functions in the categorization of Uyghurs as “Eurasian” and “mixed East Asian and European” populations. It also shows how synecdoche and metonymy function through syllogisms to mediate relations and the exchange of objects of value such as Uyghurs’ genetic materials and data between the phases and sub-narratives of the scientific articles’ narrative schema.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".