MétaCan
Menu
Back to cohort
Record W4398238117 · doi:10.1257/pandp.20241132

DOJ Intervention and the Checkpoint Shift: Profiling Hispanic Motorists under the 287 (g) Program

2024· article· en· W4398238117 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAEA Papers and Proceedings · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProfiling (computer programming)Computer sciencePsychologyComputer securityBusinessProgramming language

Abstract

fetched live from OpenAlex

This research examines whether the Department of Justice's (DOJ's) investigation into the Alamance County Sheriff's Office, a 287(g) program participant, influenced the policing behavior of other 287(g)-participating agencies in North Carolina. The study reveals that these agencies increased stops of Hispanic drivers at checkpoints following the DOJ lawsuit, indicating a strategic shift in response to potential DOJ scrutiny. Our findings suggest a phenomenon where 287(g) agencies, under threat of investigation, modify their discriminatory strategies, perpetuating racial and ethnic disparities in policing. This adds to the understanding of 287(g) and its role in fostering racial profiling.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.267
Teacher spread0.242 · 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