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Record W2275552647 · doi:10.1080/08865655.2015.1124244

The Relation of Drug Trafficking Fears and Cultural Identity to Attitudes Toward Mexican Immigrants in Five South Texas Communities

2016· article· en· W2275552647 on OpenAlexvenueno aff
Manuel Ramírez, Nanci L. Argueta, Yessenia Castro, Ricardo Becerra Pérez, Darius B. Dawson

Bibliographic record

VenueJournal of Borderlands Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsDrug traffickingImmigrationMetropolitan areaIdentity (music)WelfareSociologyQualitative propertyCriminologyQualitative researchGender studiesSocial psychologyPsychologyPolitical scienceGeographyAnthropologyLaw

Abstract

fetched live from OpenAlex

This paper reports the findings of research investigating the relationship of spill-over fears related to drug trafficking and of cultural identity to Mexican Americans' attitudes toward recent immigrants from Mexico in five non-metropolitan communities in the US-Mexico borderlands of South Texas. A mixed methods design was used to collect data from 91 participants (30 intact families with two parents and at least one young adult). Quantitative findings showed that the majority of participants expressed the view that most people in their communities believed that newcomers were involved in drug trafficking and in defrauding welfare programs. A significant interaction indicated that Mexican cultural identity buffered the negative effects of drug trafficking fears as related to the view that the newcomers were creating problems in the communities and region. Qualitative data yielded positive and negative themes, with those that were negative being significantly more numerous. The findings have implications for intra-ethnic relations in borderlands communities as well as for immigration policy.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.071
GPT teacher head0.400
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 teacher head, 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

Citations3
Published2016
Admission routes1
Has abstractyes

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