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Record W3000283495 · doi:10.1108/ijlm-02-2019-0062

Corruption, gender inequality and logistics performance

2020· article· en· W3000283495 on OpenAlexaff
Paul D. Larson

Bibliographic record

VenueThe International Journal of Logistics Management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLanguage changeInequalityTransparency (behavior)Per capitaEconomicsGross domestic productIndex (typography)Public economicsEconomic growthSociologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to develop and test theory-driven hypotheses on the influence of corruption and gender inequality on logistics performance. Design/methodology/approach This paper develops hypotheses based on a review of the literature and theory linking corruption, gender inequality and logistics performance. Testing the hypotheses draws on the following secondary data sources: the World Bank Logistics Performance Index, Transparency International’s Corruption Perceptions Index and the United Nations Development Programme Gender Inequality Index. Regression analysis is used to test the hypotheses. Findings A significant direct effect is evident between corruption perceptions and perceived logistics performance. Corruption is detrimental to logistics. Further, there is evidence of an indirect effect, via gender inequality. Gender inequality is also linked directly to lower logistics performance. Gross domestic product/capita enters the analysis as a control variable. Research limitations/implications While the analysis uses secondary data, sources are credible and their methods – while not perfect – are logical and appear to be reasonable. It is possible that excluded variables could further explain the relationships under study. This implies future research opportunities, perhaps involving case studies of specific nations. Practical implications The results should inspire businesses, non-governmental organizations and governments to invest in, aid, advocate for and legislate toward greater gender equality – and against corruption. Logistics educators have an important role in disseminating this message. Social implications Gender inequality and corruption are current, global social issues. Moving forward toward equality and away from corruption are the right moves. Such moves appear to also yield better logistics. Originality/value This paper is among the first linking corruption and gender inequality to logistics performance. It shows how social issues impact logistics performance at a national level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.177
GPT teacher head0.281
Teacher spread0.104 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2020
Admission routes1
Has abstractyes

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