On Features of Legal Terms Applied in the Criminal Case News: Based on Xinran Ji Reports
Bibliographic record
Abstract
The legal terms stand as an essential part of legal language representing specialized concepts in laws, acts and regulations, whose distinguishing features remains, even with the applications in other non-legal instruments, and keep affecting the writing style of legal instruments, explicitly, the case news. Criminal cases are the main staples reported in case news, because people’s preferences, social controversies and public policies are able to be indicated in criminal cases. Furthermore, criminal cases can also inspect senses of social justice and morality in a more profound perspective, therefore the criminal case news is more valuable than the civil case news. But relatively speaking there are numerous legal terms being involved in criminal news reports, which requires reporters or editors should posses the corresponding attainment of legal knowledge, master the features of legal language and precisely understand the relation between the legal terms and the news story so that the news events could be represented in the presence of readers objectively and veritably, at the same time the communication effects made by news media could be actually exerted. This paper selects news reports of the case of Chinese graduate Xinran Ji studying in America murdered from media as research texts, which combine with features of the legal terms and criminal case news to add up and analyze the legal terms utilized in news reports on the case. In the end, it is hoped that the summary on features of the legal term applied in criminal case news would be drew out to contribute some inspirations and reflections.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".