Sensing probation in Canada: Notes on affect and penal aesthetics in risk assessment
Bibliographic record
Abstract
Based on 6 years of probation practitioner experience in a metropolis of Canada, I provide an autoethnographic account reflecting on my fieldwork as I now commence doctoral studies. Contributing to discussions of experience in the penal atmosphere, I explore personal ethics and values, looking specifically to LSI-R software, where my experience with risk-based programming indicates a subjugation of both supervisees and supervisors. Studying penal aesthetics within the version of the software I used to assess criminogenic risk thus elucidates why evaluators tend to score their risk ratings upward rather than downward. Implications for a desistance paradigm are juxtaposed to the RNR model of offender management, where sensing visual and haptic stimuli pertains to an algorithmic governance mode limiting human connection. I conclude by reflecting on organisational values and behaviour to indicate where therapeutic alliances with criminalised people intersect criminalisation and desistance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".