Negotiating Prison Life: How Women “Did Time” in the Punitive Era of the 1990s
Bibliographic record
Abstract
to this point , our analysis of women's imprisonment in California has concentrated on how temporal and institutional factors shape prisoners' responses to their carceral environments. We found that both factors are important. The California Institution for Women (CIW) in the 1990s had only partial success in translating the goals of the new punitive era to its inhabitants largely because of its tenure and legacy in California's imprisonment history; but at Valley State Prison for Women (VSPW), an institution whose tenure began in the 1990s, the effects of the recent transformations in penalty are clearly more discernable. In this final chapter, we consider the extent to which the kinds of institutional distinctions we have drawn remain important for how women do time once we take into account women's personal attributes and life experiences. To do this, we expand our analysis, which to this point has largely relied on interviews, to incorporate information we obtained from our respondents to the surveys we administered. We develop a measure of, or operationalize the concept of, “doing time” by using latent class analysis. While less nuanced than our previous analyses, in what follows we hope to provide both a more representative picture of how women do time in the 1990s and a more complete description of the factors that help to determine their different responses to imprisonment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".