Under Emanuel, Chicago has fewer detectives to solve murders
Bibliographic record
Abstract
A WBEZ analysis of government records has found that under Mayor Rahm Emanuel, fewer murders are getting solved and the city has fewer detectives and forensic investigators working to solve them. More murders are going unsolved and Chicago's got fewer detectives and forensic investigators working to solve them. A WBEZ analysis of government records's found Since Mayor Rahm Emanuel took office, the number of detectives on Chicago's payroll is down by nearly 20 percent. That means there's 300 fewer cops investigating homicides in Chicago this year compared to back in 20-10. The number of forensic investigators's is down even further. There used to be 36 on staff, now there's only 11 -- in a city where more than 26 hundred people were shot last year. When it comes to the number of murders getting solved, WBEZ found that more than 70 percent of murders go unsolved in calendar year they're committed, and even if you count solving murders from past years, the clearance rate for Chicago is around it's lowest level in decades and FBI data shows Chicago compares poorly with other big cities when it comes to the percentage of murders it solves. And actually, even in cases marked cleared, the killer isn't necessarily found. About a quarter of cases cops've cleared under Emanuel were the result of things like dropped charges, the suspect dying or leaving the country, and other kinds of what're called exceptional clearances.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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; both teacher heads agree on what is shown here.
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".