The Luck of the Draw: Using Random Case Assignment to Investigate Attorney Ability
Bibliographic record
Abstract
One of the most challenging problems in legal scholarship is the measurement of attorney ability. Measuring attorney ability presents inherent challenges because the nonrandom pairing of attorney and client in most cases makes it difficult, if not impossible, to distinguish between attorney ability and case selection. Las Vegas felony case data, provided by the Clark County Office of the Public Defender in Nevada, offer a unique opportunity to compare attorney performance. The office assigns its incoming felony cases randomly among its pool of attorneys, thereby creating a natural experiment free from selection bias. We find substantial heterogeneity in attorney performance that cannot be explained simply by differences in case characteristics, and this heteroge-neity correlates with attorneys’ individual observable characteristics. Attorneys with longer tenure in the office achieve better outcomes for the client. We find that a veteran public defender with ten years of experience reduces the average length of incarceration by 17 percent relative to a public defender in her first year. While we find no statistical difference based on law school attended or gender, we find evidence that the public defender’s race correlates with sentence length, with Hispanic attorneys obtaining sentences that were up to 26 percent shorter on average than those obtained by black or white attorneys. We also find evidence suggesting that differences in sentencing may be driven partly by different plea bargaining behavior on the part of the public defenders.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".