Bibliographic record
Abstract
Objectives: Self-efficacy, the subjective belief that one can successfully perform a behavior, has been shown to be an important predictor of various conventional behaviors. This study applies self-efficacy theory to offending experiences and aims at examining the factors that influence criminal self-efficacy. Methods: The study is based on a survey questionnaire that was administered to 212 inmates. The sources of information identified by self-efficacy theory—individual and contextual characteristics, physiological states, social persuasion, vicarious learning, and personal performance accomplishments—were operationalized with the data to evaluate their impact on criminal self-efficacy. Results: Results from ordered logistic regressions demonstrate that age, education, legitimate earnings, relative criminal earnings, qualifications, authority, and criminal earnings are the most potent factors influencing the development of criminal self-efficacy. Conclusion: This study’s findings are consistent with research on noncriminal contexts in that one’s self-efficacy in a given domain is primarily the result of personal and vicarious experiences as well as contextual features surrounding these activities. While this study could not evaluate the temporal horizons extending from criminal self-efficacy, we believe that these subjective outlooks bare great theoretical relevance for life course criminology and might prove informative in understanding criminal persistence and desistance.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".