An analysis of keywords used in the literature on green supply chain management
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 analyze the keywords used in peer-reviewed literature on green supply chain management. Design/methodology/approach To determine the keywords that were used in this area, an analysis of 629 papers was conducted. The papers were identified through searches of 13 keywords on green supply chains. Trends in keyword usage were analyzed in detail focusing on examining variables such as the most frequently used journals/keywords, their frequencies, citation frequency and research contribution from different disciplines/countries. Findings A number of different terms have been used for research focused on the environmental impacts of supply chains, including green supply chains, sustainable supply chains, reverse logistics and closed-loop supply chains, among others. The analysis revealed that the intensity of research in this area has more than tripled in the past six years and that the most used keyword was “reverse logistics”. The use of the terms “green supply chains” and “sustainable supply chains” is increasing, and the use of “reverse logistics” is decreasing. Research limitations/implications The analysis is limited to 629 papers from the Scopus database during the period of 2007 and 2012. Originality/value The paper presents the first systematic analysis of keywords used in the literature on green supply chains. Given the broad array of terms used to refer to research in this area, this is a needed contribution. This work will help researchers in choosing keywords with high frequency and targeting journals for publishing their future work. The paper may also provide a basis for further work on developing consolidated definitions of terms focused on green supply chain management.
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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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