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Record W3093450747 · doi:10.22329/csw.v21i2.6462

The Criminalization of Immigration and Intellectual Disability in the United States

2020· article· en· W3093450747 on OpenAlexvenueno aff
Lauren A. Ricciardelli, Larry Nackerud, Adam Quinn

Bibliographic record

VenueCritical Social Work · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationIntellectual disabilityImmigrationDemocracyCriminologyStigma (botany)Political scienceSociologyLawPsychologyPoliticsPsychiatry

Abstract

fetched live from OpenAlex

Public attitudes, negative stereotypes, and stigma are essential to cultural narratives about the membership status of people with intellectual disability and people who have immigrated to the United States. With a concern for the exclusion of people from participation in democratic societies, this mixed methods study explores conceptual links between the criminalization of intellectual disability and immigration. The overlay of criminal justice norms and practices onto civil law without parallel adoption of safeguards results in the misallocation of risk to individuals with out-group membership status. This study offers conceptual analysis by applying to the policy issue, standard of proof of intellectual disability in death penalty cases, the framework of membership theory and related constructs present in the scholarly literature on immigration policy. Exact measures logistic regression is used to predictively link states’ standards of proof of intellectual disability with immigration status. It was found that the best model predicting the probability of a Higher than Preponderance standard of proof of intellectual disability in death penalty cases was a two-variable model consisting of Prior Ban and Unauthorized Immigration. This study presents recommendations for research and policy practice.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 head0.048
GPT teacher head0.351
Teacher spread0.304 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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