Mapping the State of the Art on Green Logistics and Institutional Pressures: A Bibliometric Study
Bibliographic record
Abstract
This article aims to build knowledge on the theme “green logistics” and “institutional pressures”, focused on identifying opportunities on its research topic. We used the ProKnow-C intervention instrument, resulting in the selection of 11 relevant articles that came to represent the bibliographic portfolio. Therefore, the bibliometric indicators based on the most prominent journals, the impact factor, the number of citations, the origin of the research centers, the research methods/tools, the most used terms and the subjects covered were used to analyze the articles selected. research in the form of networks. The results showed that the most prominent journal is the International Journal of Production Economics; the article with the largest number of citations (343 citations) is written by Sameer Kumar and Valora Putnam. In relation to the origin of the research centers there was a diversity of institutions of various nationalities, the USA being the country with the largest number of institutions, followed by United Kindon and Malaysia. As for the research methods, we have identified literature reviews, case studies, surveys, conceptual framework proposal and monitoring system development. In relation to the mapping and research networks, we highlight terms such as logistic, regulatory pressure, practice, driver, economic performance, institutional pressure, among other relevant terms. In this context, this information can “shed light” on interested parties and researchers on the subject in order to conceptualize, interpret and visualize their relevance, as well as the coverage networks and related researches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.181 | 0.251 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".