A Contrastive Study of Heteroglossia in the Reasoning of Criminal Judgments of Chinese Mainland and Hong Kong
Bibliographic record
Abstract
Within the framework of the Engagement System of Appraisal Theory, this thesis investigates the heteroglossia of the reasoning of criminal judgments of Chinese mainland and Hong Kong and examines their similarities and differences in the employment of heteroglossic engagement resources and underlying causes. The reasoning of 20 criminal judgments of Chinese mainland and Hong Kong produced upon second instance for the same cause of action are collected and built as two separate corpora to carry out the study. It is found that judges of both Chinese mainland and Hong Kong employ various heteroglossic engagement resources to locate position and negotiate with other voices while proceeding with reasoning. Furthermore, they share some similarities in the selection of subtypes of engagement resources, which is attributed to the fact that they hold similar communicative purposes in the reasoning of judicial judgments; while the differences can be interpreted from the distinct legal doctrines in the mainland and Hong Kong and the textual structure of the reasoning of criminal judgments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".