Does evidence really matter? An exploratory analysis of the role ofevidence in plea bargaining in felony drug cases.
Bibliographic record
Abstract
The majority of cases in the United States are disposed of through plea bargaining; however, this important discretionary point has received relatively little attention from researchers compared with trial and jury proceedings, and other discretionary points such as arrest and sentencing. Additionally, although evidence is considered an important factor in determining case outcomes, its influence on prosecutors' decisions regarding plea offers is less clear. In this study, we examined the potential impact of evidentiary factors, as well as other legal and extralegal factors, on two plea bargaining decisions, plea-to-a-lesser-charge offers and sentence offers, using data on felony drug cases processed by the New York County District Attorney's office. We found that prosecutors made more punitive charge offers when they had audio/video evidence, eyewitness identification(s), prerecorded buy money used by an undercover officer in a buy-and-bust operation, or had recovered currency. Of all evidence factors analyzed, only the recovery of currency predicted sentence offers. By contrast, three other factors-defendants' detention status, the presence of multiple plea offers, and prior prison sentence-had a much greater impact on charge and sentence offers. Although additional research is needed, it is possible that evidence has a greater impact at the initial stages of a case, particularly on the decision about whether to accept a case for prosecution, than it does on subsequent prosecutorial decisions.
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 | 0.044 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".