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Record W4285418733 · doi:10.55763/ippr.2021.02.04.004

Breaking the Law

2021· article· en· W4285418733 on OpenAlexaff
Anirudh Tagat, Nikhil George, Nidhi Gupta, Hansika Kapoor

Bibliographic record

VenueIndian Public Policy Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSpringboard (Canada)
Fundersnot available
KeywordsScope (computer science)Law enforcementEnforcementNorm (philosophy)ProductivityPsychological interventionBusinessAnti-social behaviourLaw and economicsPublic economicsPolitical scienceLawTransport engineeringComputer scienceEconomicsSociologyEngineeringEconomic growthCriminologyPsychology

Abstract

fetched live from OpenAlex

This paper adapts existing theoretical frameworks of social norms and their interactions with laws to study the case of rule violations in Indian road traffic. Specifically, we look at the case where existing laws and rules are violated with such regularity that breaking the law becomes the social norm. We investigate this framework in the case of road user behaviour in (urban) India, where road safety and traffic violations have been the focus of recent policy changes. We propose that a lack of road discipline and traffic violations have an impact on road safety, as well as on congestion. These, in turn, have implications for the economic productivity and development of a country, as well as the well-being of its citizens. Our application of the framework suggests conditions of enforcement under which such harmful social norms can be reversed. Policy interventions and scope for behaviorally-informed policies targeted at improving road user behaviour are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.036
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.109
GPT teacher head0.438
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

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