Telecoupling in the Food Supply Chain: Analysis of Trends and Gaps in the Literature
Bibliographic record
Abstract
This study aims to shed light on gaps in research on telecoupling in the food supply chain. Both the Bibliographic Coupling and the Co-word analysis were applied to that end. Using broad search terms, we screened titles and abstracts in the Web of Science (WoS) and Scopus databases, categorising results by main topics. We synthesised relevant literature on each topic to provide a comprehensive overview. Key methodological steps included database selection, publication screening, exclusion criteria application, and a timeframe from 2013 to 2023. VOSviewer, bibliometric and scientometric analyses were conducted, revealing clusters of interconnected terms in co-word networks for each database. Results indicated recurring keywords across research areas. In co-citation analyses, WoS exhibited 35 references. Scopus showed limitations with only four co-cited references. The Bibliographic Coupling method highlighted shared theoretical bases among publications, emphasising common topics. Both databases offer distinct advantages, and the choice between them depends on the specific research focus. WoS provided a larger quantity of results for our topics. The Bibliographic Coupling method (co-cited references) resulted in eight papers. We identified significant research gaps in the literature on transportation flows within telecoupling in the food supply chain. Addressing these gaps could enhance the understanding of telecoupling dynamics and their impact on sustainability. The findings provide a foundation for future research and inform policy making in this area.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.051 | 0.098 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".