Bibliographic record
Abstract
China has long been sceptical on mitigating sentences based on the offender's good character, while good character mitigation is widely accepted in Canada.This study was to examine the justification of good character mitigation in Canada so that China can better face the future choice in sentencing: whether to consider good character a mitigating factor.Through examining the use of good character in Canadian sentencing practice, the justification of good character mitigation in Canada has been questioned.A three-part argument has been put forward to support the removal of good character as a mitigating factor in Canada: first, the workability of the very concept of -good character is low; second, theoretical basis for mitigating sentences on good character is problematic; third, the present practice contributes to a form of status-based discrimination.This study shows that the justification of good character mitigation is not as strong as we have expected it to be.Even in Canada, a country which has good character mitigation with a long history, the consideration of good character as a mitigating factor needs further discussion.The adoption of good character mitigation in China should be more cautious.Last but not least, to my husband, Liezhou Zhu, who took good care of me while I was busy on my thesis writing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".