Invisible Victims: Homeless and the Growing Security Gap. By Laura Huey (Toronto: University of Toronto Press, 2012, 174pp. 38.99)
Bibliographic record
Abstract
Ned Polsky argued about criminologists that they spent too little time talking to criminals, and hence could not be said to be in a position to understand crime at all. His was an argument for ethnography—an approach that aims to capture the life world of groups, societies, subcultures and sects. In this respect, Laura Huey’s book, Invisible Victims: Homelessness and the Growing Security Gap, looks promising. Based on sustained ethnographic research from her PhD, from work across two continents, and with a refreshing abstinence from protestations of objectivity, her book aims to demonstrate how security is denied to homeless people. It is denied not just because the condition of homelessness is de facto insecure (based as it is on a lack of housing and economic security), not just because homelessness exposes people to violence and other forms of victimization, but also because basic protections afforded by the state—particularly the police—are denied to them because security is unequally distributed. In this respect, this book’s perspectives sit alongside contemporary analyses in criminology described in Huey’s first chapter on punitivity, exclusion and securitization as well as citizenship studies. Her basic argument is that security is an essential part of citizenship and that the ‘existence of the security gap that homeless citizens face tells us much about the relative state of citizenship within contemporary liberal democracies’ (p. 5). The fact that security and basic protections are denied to homeless people ‘is indicative not only of the stratified nature of society but the stratified nature of citizenship and civil rights’ (p. 6).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".