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Record W4395010677 · doi:10.6000/1929-4409.2024.13.09

Modernization Theory Revised: Testing the Relationship between Inward Foreign Direct Investment and Homicide

2024· article· en· W4395010677 on OpenAlexvenueno aff
Philip J. Levchak

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHomicideModernization theoryForeign direct investmentForensic engineeringCriminologyPsychologyEconomicsEngineeringMedicinePoison controlInjury preventionLawPolitical scienceMedical emergencyMacroeconomics

Abstract

fetched live from OpenAlex

Purpose: Modernization theory suggests that economic development is temporarily disruptive to social life and can lead to crimes of violence such as homicide. However, few studies have considered how the modernization process works. Specifically, they neglected the role of globalization. Previous research has suggested that certain measures of globalization may be theoretically linked to homicide. This study examines how inward Foreign Direct Investment (FDI), a key component of globalization and economic development, is associated with cross-national homicide rates.
 Methods: Data from 101 countries were collected and analyzed to examine the relationship between inward FDI and homicide. Indirect effects of inward FDI on homicide through urbanization and economic growth were also examined.
 Results: The results show that inward foreign direct investment increases cross-national homicide rates, both directly and indirectly through increased urbanization.
 Conclusion: While economic development benefits society, the concomitant, deleterious effects should be considered by policymakers, especially those seeking inward foreign direct investment in their countries. Future researchers will want to consider examining other measures of globalization.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.209
GPT teacher head0.368
Teacher spread0.159 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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