Mapping the State of the Art on Green Logistics and Institutional Pressures: A Bibliometric Study
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
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.027 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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