A Study of Supply Chain Research During the Covid-19 Pandemic Using Text Mining and Bibliometric Methods
Bibliographic record
Abstract
The Covid-19 pandemic has been one of the significant factors in supply chain disruptions worldwide, and research in this domain has gained considerable attention during the last few years. However, it is crucial to understand the amount of attention each industry and problem gets to improve the quality of future research and identify research gaps. In this literature survey, 574 research papers from the Scopus database were retrieved and pre-processed to perform the analyses. A bibliometric study using keywords was conducted to unveil the various research themes in the current literature related to supply chains and Covid-19. Moreover, topic modelling was implemented using abstracts to contrast the results from the bibliometric analysis. From the studies, the frequent research streams are identified, clustered, and discussed. Moreover, future research avenues are highlighted. Additionally, it can be observed that both methodologies complement each other. On one side, the bibliometric study provides a broad perspective of the topics; on the other side, the modelling reveals more detailed themes. Hence, researchers could employ both approaches simultaneously when performing an analysis of the state-of-the-art to present an enriched research panorama. This study also highlights the use of both methods to accelerate the review process and attain better insights.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.099 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".