Understanding carceral mobilities in and through lived experiences of incarceration
Bibliographic record
Abstract
Recent scholarship on carceral mobilities critiques conceptualizations of carceral spaces as fixed and stable, and movements within or around sites of confinement as linear and horizontal. According to this critique, criminological studies of imprisonment have typically embraced what Turner and Peters (2017) [‘Rethinking mobility in criminology’, Punishment & Society 19(1), 96–114] term a ‘sedentarist ontology’ by failing to consider the complexities of prisoner mobilities in the lived experiences of the carceral. We draw on qualitative interview data from the Prison Transparency Project, a multiyear study initially across four research sites in Canada focused on former prisoners’ narratives of their carceral experiences, to identify and analyze the multifaceted mobilities that characterize prison life. We focus on three aspects of carceral mobilities: the use of psychotropic medications to produce docility, the coercive (im)mobilities of physical restraints and the ‘prison on wheels’ (i.e., prisoner transport vehicles). Using the concept of ‘kinetic immobility’, in which prisoners’ bodies are immobilized so they can be coercively moved (or not) through space and time, we consider the degree to which the theoretical work on carceral mobilities aligns with lived experiences of incarceration, as narrated by research participants.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".