COMMUNITIES, STREET GUNS AND HOMICIDE TRAJECTORIES IN CHICAGO, 1980–1995: MERGING METHODS FOR EXAMINING HOMICIDE TRENDS ACROSS SPACE AND TIME*
Bibliographic record
Abstract
We merge Exploratory Spatial Data Analysis (ESDA) and a semi‐parametric, group‐based trajectory procedure (TRAJ) to classify communities in Chicago by violence trajectories across space. Total, street gun and other weapon homicide trajectories are identified across 831 census tracts between 1980 and 1995. We find evidence consistent with a weapon substitution effect in violent neighborhoods that are proximate to one another, a defensive diffusion effect of exclusively street gun‐specific homicide increases in neighborhoods bordering the most violent areas, and a spatial decay effect of temporal homicide trends in which the most violent areas are buffered from the least violent by places experiencing mid‐range levels of lethal violence over time. In merging these two methods of data analysis, we provide a more efficient way to describe both spatial and temporal trends and make significant advances in furthering applications of space‐time methodologies.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".