Education, Policy, and Juvenile Delinquents: A Mixed Methods Investigation During COVID-19
Bibliographic record
Abstract
COVID-19 mitigation efforts resulted in many schools making the transition to online and remote instruction. Juvenile delinquents, as a group, attained lower academic achievement before the pandemic, and little was known how juvenile delinquents’ education fared after schools ceased face-to-face instruction. Using a mixed methods approach, three steps were conducted to analyze the education of juvenile delinquents in the United States: a qualitative literature review, a grounded theory study of teachers’ concerns in traditional schools, and an instrumental case study of juvenile delinquents’ enrollment during COVID-19. Researchers and experts recommended the development of a community online and in remote instruction, but most teachers felt overwhelmed and unable to rise to the challenge. Juvenile delinquents responded by most students disappearing from school attendance rolls. A grand theme, to shift the nature of online learning, is offered based upon the convergence of the research findings. A theory of humanistic schooling online, centered on a community of learners with the dimensions of academics, physical health, social, and attention to the individual, offers to radically transform practices and past recommendations.
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 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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".