Bibliographic record
Abstract
Extract "Dr. Ruback's authoritative volume is a must-read for scholars and policy-makers interested in shaping behavior through economic sanctions. Impressively, Dr. Ruback's book summarizes and integrates 20 years of interdisciplinary empirical research on economic sanctions by his and others' research teams to develop cogent and practical policy recommendations." —Brian Cutler, Professor, Ontario Tech University "Dr. Ruback has been the leading expert on economic sanctions in the US for decades. This book is an essential resource for anyone worried about the financial penalties that are stacked on to people who get caught up in American criminal justice systems. Many are driven more deeply into poverty, which cannot be good for their successful reintegration into society." —Kevin R. Reitz, James Annenberg La Vea Professor of Law, University of Minnesota "A wide-ranging study of the ethical, criminological, legal and policy implications of economic sanctions in US criminal justice. By combining this analysis with extensive empirical research into all phases of the imposition of money sanctions, Ruback provides an invaluable addition to the rapidly growing criminological literature on fines, restitution and related penalties in the criminal justice system."
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".