Bibliographic record
Abstract
IN A SKILLFULLY CRAFTED ANALYSIS OF THE EXPANDING PRISON-INDUSTRIAL COMPLEX, Sudbury encourages us to connect the dots between a number of economic, political, and social patterns that are traditionally examined in isolation. Although substantial academic and activist attention has been devoted to the growth of the prison-industrial complex in the U.S., very little has been done to examine this trend in its global context, and even less work has addressed the links between mass incarceration, global capitalism, and U.S. militarism. A comprehensive, systematic analysis of these relationships is a daunting, Hercu? lean task and Sudbury provides a framework for doing just that. Sudbury's piece is an important contribution to the relatively recent body of work that insists on broadening the analysis of incarceration policies and practices to a global level. Building on an earlier article (Sudbury, 2000), her work moves beyond a narrow ideological analysis and begins the process of demonstrating the multiple layers of interdependence and symbiotic reinforcement between U.S.-led global capitalism, militarism, and the international growth of the prison-industrial complex. Sudbury embraces the theoretical framework offered by the concept, industrial complex, and adopts a transnational feminist approach in her work that centers on the lives of women of color and Third World women. Arguing that the prison-industrial complex model encourages us to look beyond single factor explanations for prison expansion, she begins the process of transform? ing abstract connections between mass incarceration, global capitalism, and
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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".