Political Space and Loyalism in Ancient China: A Case Study of Li Si
Bibliographic record
Abstract
This study adopts a historical-sociological perspective to examine the interplay between political space and the discourse of loyalty in ancient Chinese historical narratives. Focusing on the biography of Li Si in Records of the Grand Historian (Shiji), it analyses the socio-political functions of key spatial settings, such as Shangcai (his hometown), Xianyang (the imperial capital), and the prison, as sites where power relations, institutional control, and moral judgment are enacted. The research argues that spatial arrangements are not merely narrative backdrops but constitute concrete manifestations of political order and mechanisms for shaping individual agency and political identity. Through moral principles, it is socially constructed and dynamically negotiated within shifting political spaces. Li Si’s spatial trajectory, from a local scholar to a central power figure and finally to a subject of state punishment, reflects the complex tension between Confucian ethics, Legalist institutions, and imperial authority. By employing spatial narrative strategies, Sima Qian constructs loyalty as a contingent and contested concept, thereby expressing deeper concerns about legitimacy, moral accountability, and historical justice. This research not only enriches the understanding of the Shiji’s narrative structure and political logic but also contributes to the sociological interpretation of ancient political culture, particularly the spatial articulation of power and ethical norms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".