Book Note: Too Big To Jail: How Prosecutors Compromise With Corporations, by Brandon L. Garrett
Bibliographic record
Abstract
OVER THE PAST DECADE, the criminal justice system has been confronted with a staggering increase in prosecutions of corporations. In Too Big to Jail: How Prosecutors Compromise with Corporations,1 Brandon L. Garrett explores the “hidden world” of corporate prosecutions2 and looks at what happens when a major company is prosecuted in the United States. Using compiled data of corporate settlement agreements and convictions from the past decade, the author reveals that prosecutors fail to effectively punish corporate crimes. Garrett draws upon his research to bring the necessary attention to corporate crime and to reflect on whether enough is being done to properly hold corporations accountable for their misconduct. In chapter one, Garrett sets the tone for the disheartening notion that large organizations are “too big to jail” and details the many challenges faced by federal prosecutors, analogizing a corporate prosecution to the Biblical battle between David and Goliath. The author examines the more lenient approaches used by federal prosecutors, noting a decline in convictions of companies accompanied by an expansion in the use of deferred prosecution and non-prosecution agreements, which are settlement agreements that focus on improving and restructuring the corporation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".