The Carceral Archive: Documentary Records, Narrative Construction, and Predictive Risk Assessment
Bibliographic record
Abstract
Over the past three decades, scholarship in sociology, criminology, law, public policy, history, science and technology studies (STS), and other allied fields has addressed various areas of concern at the intersections of race and correctional supervision in the United States. Some scholars have argued that the modern day prison industrial complex is a transparent extension of chattel slavery, while others have asserted that a ubiquity of carceral ideologies has contributed to a broader social inequality—one so affecting as to prevent people from oppressed and marginalized communities from full participation in American life. This scholarship unambiguously attests to ongoing disparities in rates of incarceration calculated along the indices of race and ethnicity; with data cultures expanding—evidenced by growth in sectors such as big data, data science, and data analytics—in the sociocultural landscapes and sociotechnical environments of the United States, more recent scholarship has focused on the ways that technology also contributes to—and often exacerbates—these disparities.
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 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".