Uninventing Carceral Technology: Four Experiments in Imagining the World More Rigorously
Bibliographic record
Abstract
How do we advance social justice in an increasingly datafied world? Against a backdrop of burgeoning social movements, data-driven technologies have become an important terrain of struggle. That’s because the design and implementation of technology is not simply about the creation of software and hardware objects, but the negotiation of practices and possibilities for living life together differently (Suchman 2007). In this dissertation, I examine the role of technology in shaping our collective imaginations of what is possible in the context of the carceral state. By carceral state, I mean the expansive system of state-sanctioned capture, confinement, and control that underpins our current unjust social order. Drawing on rich intellectual traditions such as feminist, Indigenous, and Black studies, I interrogate the default assumptions underlying the design and implementation of data-intensive systems, in order to fundamentally reimagine the role of technology in larger struggles for justice. Ultimately, the aim of this work is to experiment with ways of “un-inventing” (MacKenzie 1993) carceral technology, by reconfiguring the structural, interpersonal, and personal aspects of computation to the point that harmful algorithms are no longer created.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".