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Record W2799698651 · doi:10.1080/08865655.2018.1457976

Perceived severity of smuggling at the border of Táchira-North of Santander: Health Psychology Approach

2018· article· en· W2799698651 on OpenAlexvenueno aff
Manuel Riaño-Garzón, Nathalie Claire Raynaud, Neida Albornoz‐Arias, Rina Mazuera‐Arias

Bibliographic record

VenueJournal of Borderlands Studies · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
FundersUniversidad Simón Bolívar
KeywordsInvestment (military)Stratified samplingPsychologyPerceptionSample (material)PopulationDemographyDemographic economicsSocioeconomicsEconomicsPolitical scienceSociologyMedicinePolitics

Abstract

fetched live from OpenAlex

This study analyzed the differences in the perception of severity of smuggling between consumers and smugglers of the Colombian-Venezuelan border. A correlational-comparative design was used, and the population selected was the inhabitants of North of Santander Department, Colombia, calculating a stratified randomized sampling for 2,383 people. It was found that 6% of the sample belonged to illegal traders, being mostly men. The comparison performed by sex concluded that the level of severity perceived is greater in women, and both groups gave greater relevance to the risk of losing the economic investment front others risks of more severity. The comparison by municipalities, revealed that the capital presented lower levels of perceived severity than other territories. In addition, greater perceived severity of smuggling in young adults, heads of household, married people, traders who earned less than two Colombian minimum wages and who traded individually were identified. Finally, a positive relationship was found between the level of perceived severity and conditions such as the number of products smuggled or work income.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.081
GPT teacher head0.343
Teacher spread0.262 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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