Internal Migration in Chinese Cities: An Exploration of Youths’ Experiences of Delinquency
Bibliographic record
Abstract
Introduction. In China, urban police tend to arrest and interrogate internal migrants when crimes occur, as they believe migrant cohorts are the main cause of crime. Detecting risk factors in migrant children's delinquency is necessary in order to allow authorities to limit the scope of crime. Methods. This essay explores studies from Chicago School of Criminology, in additional to other relevant western criminological literature. This essay investigates how poor living conditions, undue levels of fear of crime, deficiency in the formation of social bonds, lack of informal control at home and school, and development of social strain are associated with migrant children's delinquency in China. Findings. Socioeconomic challenges drive migrant children to delinquency. Migrant children are subject to discrimination and exclusion at school and public spaces, in addition to segregation residentially. The unfair treatment they receive contributes to their inability to develop a metropolitan social bonds and trust. Similarly, migrant parents are victimised by social discrimination, exploitation and exclusion, minimising their opportunities to exercise positive parenting. Their economic hardships impede migrant cohorts from alleviating poverty and increasing community engagement. Local urbanites' fear of crime against migrant cohorts fosters mutual misunderstanding, mistrust and conflicts. Social tension and fear of crime reinforce local urban residents' segregation and discrimination against internal migrants. Conclusions. Migrant children may demonstrate a higher propensity of delinquency than local counterparts. However, more attention should be given to their victimisation as a result of economic hardships and social inequalities, in order to effectively exercise crime control in Chinese cities.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".