Bibliographic record
Abstract
The present chapter seeks to link two of the central facts concerning victimization by crime in the Western world. The first is that the burden of crime is borne very unequally across areas and within areas across households and individuals (Tseloni et al., 2010). The second is that there has been a very substantial cross-national drop in crime as captured by victimization surveys (van Dijk et al., 2007) (Farrell et al., 2010). The authors seek to establish whether the crime drop has resulted in a more or less equitable distribution of crime across households. Inequality of victimization challenges distributive justice. Harms as well as goods should be distributed equitably. Changes in inequality would suggest whether we should regard the crime drop as unequivocally benign (inequality reducing or neutral) or have reservations about its benefits (inequality increasing). The possible outcomes of the analysis have differing implications for criminal justice in general and policing in particular. There is already evidence that policing concentration at least in England and Wales is not proportionate to the presenting crime problem (Ross & Pease, 2008), and reasons have been suggested for this, the writers’ favoured account being labelled the “winter in Florida, summer in Alaska” paradox (Townsley & Pease, 2002). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".