Bibliographic record
Abstract
Abstract A broad understanding of crime requires explanations for both the origins of individual and group criminal propensity and when and where criminal events occur. Crime pattern theory provides explanations for the variation in the distribution of criminal events in space and time given a range of different propensities. In the organization of their everyday lives, both occasional and persistent criminals spend most of their time engaged in the same legitimate everyday activities as everyone else. The location of criminal events in space–time are shaped by these everyday activities and the specific criminal’s activity. Occasional and persistent offenders develop activity spaces and awareness spaces. The shape and dynamics of these spaces is influenced by the structures of human settlements that channel and limit movement patterns in time and space. These structures include the built environments and the socioeconomic and cultural environments in which people live, work, or go to school, and in which they spend their social, entertainment, and shopping time. Crime pattern theory utilizes the major components of the built and social environment—activity nodes, paths between nodes, neighborhoods and neighborhood edges, and the socioeconomic backcloth—in conjunction with the routine movements of the population in general to understand crime generator and crime attractor locations and the formation of repeat areas of offending for individuals and groups of offenders as well as more aggregate crime hot spots and cold spots. This information is translated into a geometry of crime that describes the journeys to crime by individual criminal offenders and groups of offenders and their victims or targets. Crime pattern theory explains the process of criminal target search, suggests strategies for crime reduction, and describes potential displacements of criminal events in space and time following changes in the suitability of targets or target locations at particular places and specific times.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".