Social Triage and Exclusions in Community Services for the Criminalized
Bibliographic record
Abstract
Abstract This article examines perspectives and practices related to social triage and the exclusion of criminalized and marginalized individuals in community services such as shelters, mental health, substance use, and court supports. Based on two years of fieldwork and interviews with 105 practitioners, I analyze narratives and practices related to working with people described as having (or being) complex, high-needs, or high-risk. I show that individual factors, such as risk, need, or responsivity, are but one type of factor considered when practitioners make decisions about triage or service eligibility. Building from theory about the governance of “risk” and “risky people,” I examine how organizational and systemic factors shape individualized understandings of and responses to risk. I argue that given current practices in under-resourced community supports, triage and resulting exclusions exacerbate social problems and contribute to punitive exclusions, especially for those who seek services, supports, or housing but have records of sexual offense, fire setting, drug use, violence, self-harm or so-called non-compliance. Examining these dynamics bolsters claims that we should shift the responsibilizing gaze upwards to pressure institutional and state bodies who could transform the landscape for practitioners and their clients.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".