Situational Action Theory: Cross-Sectional and Cross-Lagged Tests of Its Core Propositions
Bibliographic record
Abstract
Situational Action Theory (SAT) is a recently developed general action theory of crime that integrates and synthesizes existing individual and ecological explanations. SAT explicitly states that the individual’s propensity for criminal behaviour (morality and self-control) and exposure to criminogenic settings (rule breaking peers and time spent in unsupervised, unstructured activities) interact to determine whether a crime is committed. In the present article, core assumptions of SAT are tested by estimating cross-sectional and lagged models on two-wave panel data from adolescents in The Hague (The Netherlands). Generally, the findings support SAT, including the situational interaction between morality and self-control. However, the findings also raise questions about SAT. In particular, we did not find lagged effects of morality on later offending, and we found only a few significant interaction effects on offending between the two peer variables and morality and self-control. Generally, there was not much support for the SAT theory that adolescents with low morality or low self-control are more vulnerable to (situational) peer influences. The article concludes with a discussion of how additional situational peer variables may be included in SAT.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".