Corruption, gender inequality and logistics performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it