The Politics of Prevention and Government Responses to Homelessness
Bibliographic record
Abstract
Recently, the logic of public health prevention has found a foothold in research and advocacy about homelessness. From a commonsense perspective, the prevention of a social problem like homelessness is an objectively positive aim. However, in the realm of social and health policy, the concept of prevention is not simply a common-sense word. It is part of a wider set of rationalities and technologies of governance which operate in and through the institution of public health. Research demonstrates that state-driven interventions designed to advance the health of a population often pose problems for particular groups. Prevention efforts, and their differential effects, thus have the potential to illuminate how state-interventions pursued with the objective of safe-guarding the public in general may simultaneously exacerbate specific structural and systemic forms of inequality. In this article, we probe the ethical, empirical, and political dimensions of state-driven responses to the coronavirus disease of 2019 (COVID-19) public health crisis, surfacing some of the ways these interventions posed problems for people who are homeless and experience intersecting health and socio-political disparities. From this vantage point, we then look critically at moves to frame homelessness as a public health crisis, as well as government efforts to prevent homelessness by drawing on public health rationalities. Although our focus is homelessness prevention, as constructed and pursued by governments, our analysis is inspired by critical public health scholarship that challenges the apparent impartiality of prevention as a central logic and set of practices in public health contexts.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| 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".