Bounded authority: Expanding “appropriate” police behavior beyond procedural justice.
Bibliographic record
Abstract
This paper expands previous conceptualizations of appropriate police behavior beyond procedural justice. The focus of the current study is on the notion of bounded authority-that is, acting within the limits of one's rightful authority. According to work on legal socialization, U.S. citizens come to acquire three dimensions of values that determine how authorities ought to behave: (a) neutral, consistent, and transparent decision-making; (b) interpersonal treatment that conveys respect, dignity, and concern; and (c) respecting the limits of one's rightful power. Using survey data from a nationally representative sample of U.S. adults, we show that concerns over bounded authority, respectful treatment, and neutral decision-making combine to form a strong predictor of police and legal legitimacy. We also find that legal legitimacy is associated with greater compliance behavior, controlling for personal morality and perceived likelihood of sanctions. We discuss the implications of a boundary perspective with respect to ongoing debates over the appropriate scope of police power and the utility of concentrated police activities. We also highlight the need for further research specifically focused on the psychological mechanisms underlying the formation of boundaries and why they shape the legitimacy of the police and law. (PsycINFO Database Record
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".