Re-imagining Everyday Carcerality in an Age of Digital Surveillance
Bibliographic record
Abstract
This dissertation project takes an interdisciplinary approach towards theorizing how we understand new modes of incarceration and confinement in the digital age. It makes key interventions in the fields of surveillance studies, carceral studies, critical data and technology studies, ethnic and racial studies. I argue that less conventional modes of incarceration and confinement, which are enabled through technologies, the Internet and processes of datafication, conceal the everyday carceral functions that target and exploit racialized people. Chapter 1 examines mobile carceral technologies that are part of Canada’s immigration and detention system. I investigate how notions of increased freedom that are associated with carceral technologies like electronic monitoring and voice reporting do not necessarily coincide with increased autonomy. In Chapter 2, I consider the relationship between mobile phone cameras and the rise of police body-worn cameras. More specifically, I examine how policing and surveillance technologies disproportionately take aim at Black people and communities, making the mere occupation of public and digital space extremely precarious. Lastly, in Chapter 3, I challenge the notion that biometric systems and technologies are race-neutral guarantors of identity, specifically within the polemical space of the modern airport. I argue that the airport’s security and surveillance infrastructure operates according to racialized knowledges, which unofficially validate the profiling of Muslim travelers by both human and non-human operators.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".