Children Behind Bars: A Path to Reducing Pre-Adjudicative Detention in the Juvenile Justice System
Bibliographic record
Abstract
(Excerpt) In 2019, nearly 16,000 young people referred to the juvenile justice system were detained in juvenile facilities. Nearly 10,000 of them had not yet been found to have committed a crime. When it comes to youthful offenders, one might assume that courts would be inclined to exhibit leniency and favor pretrial release. In reality, judges detain youth pretrial in over a quarter of delinquency cases. Pretrial detention does not affect all youth at an equal rate: juvenile court judges consistently detain older youths more often than younger youths, more boys than girls, and far more children of color. In fact, “youth pretrial detention is marred by racial disparity.” For example, “less than 21% of white youth with delinquency cases are detained, compared to 32% of Hispanic youth, 30% of Black youth, 26% of American Indian youth, and 25% of Asian, Native Hawaiian, or Pacific Islander youth.” Like in the adult criminal justice system, pre-adjudicative detention is an inevitable reality of the juvenile system. Federal guidance provides that “the purpose of juvenile detention is to confine only those youth who are serious, violent, or chronic offenders pending legal action.” But in practice, this form of detention is decidedly not reserved for the most serious cases where, for example, a juvenile’s alleged offense is such that he poses imminent and grave danger to the community. In fact, a 2019 report found that “over 3,200 youth [were] detained [nationally] for technical violations of probation or parole, or for status offenses, which are behaviors that are not law violations for adults.” Two distinct problems are present when it comes to the pretrial detention of juveniles: detention is not limited to the most serious cases, and racial disparities abound. But while the statistics might paint a grim picture, there is hope for this vulnerable population. In the wake of robust bail reform for adults and juvenile justice reform across the country, the time is ripe for change in pre-adjudicative decision-making in the juvenile justice system. This Note will explore one specific option for such reform: integrating algorithmic risk assessment tools into judicial determinations of whether a juvenile needs to be detained before his trial. This particular reform has been successful in the adult system, and it could serve to remove racist and unjust subjectivity behind today’s preadjudicative detention of juveniles. Part I of this Note will explain the workings of the juvenile justice system and the harms that juveniles suffer when they are detained before trial. Part II will discuss the current state of reform in both the juvenile and adult criminal justice systems and will argue that the juvenile system should be further reformed by borrowing from adult system reform involving algorithmic risk assessment tools.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".