Educational achievement and youth homicide mortality: a City-wide, neighborhood-based analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Educational achievement, particularly among youth, may mitigate risk of exposure to violence and negative related health outcomes such as crime and gang activity. Few studies to date have examined relationships between education and youth homicide. The authors hypothesized association between educational achievement in grades 3 and 8 and youth homicide mortality. METHODS: Neighborhood-based, city-wide analysis was conducted of cross-sectional data regarding N = 55 neighborhoods in Baltimore, MD, extracted from Baltimore 2017 Neighborhood Health Profiles. RESULTS: Higher educational achievement (operationalized by reading proficiency) in third, but not eighth, grade was associated with reduced neighborhood youth homicide mortality rates in hierarchical linear regression, controlling for demographic and socioeconomic factors (ß = - 0.5082, p = 0.03), such that each 1.97% increase in proportion of students reading at an acceptable level was associated with one fewer neighborhood youth homicide per 100,000. Neighborhoods within the highest tertile of youth homicide mortality differed from those in the lowest tertile with fewer males (45% vs. 48%, p = 0.002), greater unemployment (17% vs. 8%, p < 0.001), familial poverty (35% vs. 16%, p < 0.001), and residents identifying as black or African-American (88% vs. 25%, p < 0.001). Causal mediation analysis demonstrated mediation effects of familial poverty and eighth grade educational achievement through third grade educational achievement (ACME = 0.151, p = 0.04; ACME = - 0.300, p = 0.03, respectively) with no significant direct effects. CONCLUSIONS: Higher educational achievement (operationalized by reading proficiency) predicts reduced homicide mortality among Baltimore youth and appears to mediate effects of familial poverty on homicide mortality as well. This converges with literature highlighting the importance of education as a determinant of social capital and violence. Future policy-based interventions should target inequalities in educational achievement to mitigate homicide risk among youth in communities facing disparities in violent crime.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".