Talented Youths or Dangerous Criminals? Exploring Judicial Attitudes in the Sentencing of Data Crime Cases in Chinese Courts
Bibliographic record
Abstract
The focal concerns framework stands as the leading theoretical approach in criminology to account for differences in sentencing outcomes. Despite extensive empirical research, this framework has yet to be thoroughly examined in the context of cybercrime or data crime and in non-Western legal cultures. This study examines the following question: to what degree and in what ways do Chinese judges take into account the original focal concerns of blameworthiness, protecting the community, and practical limitations when making their sentencing decisions? By analyzing 2,052 cases and focusing on 34 detailed sentencing remarks from cases adjudicated in China from 2013 to 2023, along with insights from semi-structured interviews with 14 judges specializing in data crime, our research indicates that sentencing decisions in data crime cases in China are consistently shaped by the blameworthiness and community protection, although rather different factors associated with these concerns impact the assessment of data crime case severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".