“I'm No Criminal, I'm Just Homeless”: The Greensboro Homeless Union's efforts to address the criminalization of homelessness
Bibliographic record
Abstract
This paper examines how homelessness is criminalized in Greensboro, North Carolina, and the ways in which the Homeless Union of Greensboro (HUG) has contested such criminalization. This paper draws on data from a participatory action research study conducted between 2018 and 2020 by a group of researchers from two local universities and members of HUG. Findings from our study suggest that law enforcement officers in Greensboro use a vast array of laws to harass, ticket, and arrest people experiencing homelessness, particularly those who are Black. Findings also suggest that when individuals experiencing homelessness seek help for citations or arrests, it is challenging to access quality, affordable legal representation. This paper illustrates how HUG takes a multi-pronged approach to address the variety of policies and practices that target homeless people, particularly people of color, recognizing that systems change requires a multifaceted approach that adapts to dynamic social and political contexts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".