The Carceral Web we weave: Carceral citizens’ experiences of digital punishment and solidarity
Bibliographic record
Abstract
This article asks: How do formerly incarcerated people navigate digital technologies? Using the metaphor of a spider web, I use 18 months of ethnographic observations of formerly incarcerated women of color to argue that formerly incarcerated people must contend with what I call— Carceral Web—the spatial intersection between carceral institutions and digital technologies. I identify two primary features of the Carceral Web: stickiness and entanglements. I characterize stickiness as the Internet’s ability to make carceral histories inescapable across time and physical space, making it impossible for formerly incarcerated people to shed their criminal histories. I characterize entanglements as the intersections of institutional carceral relationships that result from practices and norms of digital connectivity. I argue that the pervasive significance of digital connectivity to everyday life compels formerly incarcerated people to contend with the Carceral Web, but stickiness and entanglements make them susceptible to exploitation and reincarceration. I call the Carceral Web’s production of vulnerable subjects predation, which I characterize as a type of hidden sentence. I contend that despite having limited resources to navigate predation, formerly incarcerated people are tasked with co-opting the Carceral Web to build solidarity and training as a self-defense survival mechanism. Understanding the Spider of the Carceral Web as the convergence of corporations and state interests allows us to see how it feeds on the lives of formerly incarcerated people by consuming their marginalization and exclusion in the interests of racialized and gendered profit.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".