Clarifying Control in Criminology: A Proposal for Six Interacting Controls of Crime
Bibliographic record
Abstract
For over 40 years, criminologists have tried to distinguish between formal and informal social control. While formal control is synonymous with the state, informal social control remains elusive. Informal social control’s vagueness has three sources. First, there is no template guiding the definition of controls. Second, informal social control is too broadly defined and yet the formal–informal dichotomy does not account for all types of control. Third, the formal–informal dichotomy does not address how controls interact. We address the sources of vagueness by increasing clarity in three ways. First, we propose six elements that all definitions of control must meet. Second, we use these elements to define six types of control: state, place management, organization, intimate, self, and stranger. These six types give greater specificity than the formal–informal dichotomy, while preserving key ideas in the original. Third, we show how these six controls can interact. The result is a set of controls, with clear definitions, that criminologists can study scientifically.
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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 | 0.041 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.007 | 0.129 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".