MétaCan
Menu
Back to cohort
Record W4379743106 · doi:10.1111/lasr.12653

Legitimacy and online proceedings: Procedural justice, access to justice, and the role of income

2023· article· en· W4379743106 on OpenAlexfundno aff
Avital Mentovich, J.J. Prescott, Orna Rabinovich‐Einy

Bibliographic record

VenueLaw & Society Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
FundersIsrael Science FoundationUniversity of TorontoUniversity of Michigan
KeywordsLegitimacyProcedural justiceEconomic JusticePerceptionPolitical scienceLaw and economicsPublic relationsLawSocial psychologyCriminologySociologyPsychologyPolitics

Abstract

fetched live from OpenAlex

Abstract Courts have long struggled to bridge the access-to-justice gap associated with in-person hearings, which makes the recent adoption of online legal proceedings potentially beneficial. Online proceedings hold promise for better access: they occur remotely, can proceed asynchronously, and often rely solely on written communication. Yet these very qualities may also undermine some of the well-established elements of procedural-justice perceptions, a primary predictor of how people view the legal system's legitimacy. This paper examines the implications of shifting legal proceedings online for both procedural-justice and access-to-justice perceptions. It also investigates the relationship of both types of perceptions with system legitimacy, as well as the relative weight these predictors carry across litigant income levels. Drawing on online traffic court cases, we find that perceptions of procedural justice and access to justice are each separately associated with a litigant's appraisal of system legitimacy, but among lower-income parties, access to justice is a stronger predictor, while procedural justice dominates among higher-income parties. These findings highlight the need to incorporate access-to-justice perceptions into existing models of legal legitimacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.360
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueLaw & Society ReviewSame topicJudicial and Constitutional StudiesFrench-language works237,207