Procedural Justice Concerns and Technologically Mediated Interactions with Legal Authorities
Bibliographic record
Abstract
The use of surveillance technologies by legal authorities has intensified in recent years. As new data collection technologies expand into law enforcement spaces previously dominated by interpersonal interactions, questions emerge about whether the public will evaluate interpersonal and technologically mediated interactions with legal authorities in the same ways. In an analysis guided by procedural justice theory, we examine whether and how legal authorities’ use of decision-making technology affects public evaluations of an authority-subordinate interaction and its outcome in the context of airport border crossings. Using an experimental vignette design (N = 278), we varied whether an encounter between a traveller and border security “agent” that produced a secondary search was described as interpersonal (conducted by a human agent) or technologically mediated (conducted by a machine agent). We also varied the traveller’s group membership relative to the nation-state, describing the traveller as either born in the country in question and a member of the nation’s most common racial group (in-group) or not born in the country and a racial minority (out-group). Both encounter type and group membership independently affected perceptions of the interaction (procedural justice judgements) and its outcome (distributive justice judgments). Technologically mediated encounters improved perceptions of procedural and distributive justice. Further, procedural justice judgments mediated the relationship between encounter type and distributive justice, demonstrating how perceptions of interactions influence perceptions of the outcomes of those interactions. Out-group members were evaluated as having worse experiences across all measures. The findings underscore the importance of extending tests of procedural justice theory beyond interpersonal interactions to technologically mediated interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".