“The Struggle is Real”: Punitive assessment in community services
Bibliographic record
Abstract
Assessment tools are pivotal for the work of frontline community services providers, shaping client relationships, access to supports and producing evidence for agencies that need to allocate resources, demonstrate outcomes and secure funding. These tools are combined and used cumulatively, as marginalized individuals are cared for – but also controlled and punished - within these systems (e.g. in shelters, street outreach, mental health or re entry supports). Punishment literature has clarified that risk tools are impactful but also contested and resisted. Still, we know little about how the process is experienced and negotiated by frontline by practitioners working with people pushed through the ‘revolving doors'. Drawing from two years of ethnographic fieldwork and 105 interviews with community practitioners, I examine tools and practices used to ‘assess’ criminalized and marginalized individuals. I show that practitioners are producing evidence about problems occurring outside legal institutions while relying on criminal justice logics and engaging with criminal justice spaces and paces. I highlight the challenges service providers face and negotiate, focusing on three themes: the composition of tools, the process of using them, and the service context in which they are used. I argue that despite discretionary efforts and adaptations, community practitioners remain frustrated by assessment tools and practices, and particularly by their inability to meet the needs they are assessing.
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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 | 0.046 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.062 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".