How to End Mass Imprisonment: The Legal and Cultural Strategies of Bryan Stevenson
Bibliographic record
Abstract
Bryan Stevenson’s Just Mercy, which is part legal history and part memoir, arrives at a moment when the tides may be turning in US criminal justice. Stevenson is a singular catalyst in the emergence of a movement against mass imprisonment, and the topics he is focused on are central to the prospect of lasting systemic reform. In his work as litigator, professor, and public figure, Stevenson has helped to usher in a new common sense that far-reaching reforms to US criminal justice are both required and imminent. Stevenson’s work becomes all the more significant when we consider the scope of change that structural reform requires. He has helped to draw the US Supreme Court away from a stance of extreme deference to legislative judgment in non-capital sentencing review – a meaningful shift in the direction of legal limits on the politics of tough punishment. This review contextualizes the publication of Just Mercy as a component of Stevenson’s legal and cultural strategies aimed at consolidating the reform movement against US mass imprisonment.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".