Predictors of Individuals' Behavioral Characteristics of Routine Activities Theory: Analysis of a Synthesis Model of Socio-Economic Status, Victimization, and Fear of Crime
Bibliographic record
Abstract
Researchers have studied victimization, fear of crime, and individuals’ behavioral characteristics to investigate the origin of crime and victimization, such as the routine activity theory. However, little research has examined how the behavioral characteristics were formed in theory. Although the elements of socioeconomic status, victimization experience, and fear of crime are believed to cause differences in human behaviors, the current study attempts to examine which predictors construct behavior characteristics like the routine activity theory, including target suitability and guardianship. Using the most recent, nationally collected official crime victimization data from South Korea (Korean Crime Victimization Survey, 2014), the study analyzed the variables with statistical models. The results suggest the following:(1) an individual’s socioeconomic status – such as gender, age, and education level – rather than victimization experience or fear of crime, are significant predictors of target suitability;(2) higher levels of fear of crime predict higher levels of guardianship; and (3) the victimization experience did not predict either target suitability or guardianship.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".