Systematic Literature Reviews in Supply chain resilience: A Systematic Literature Review
Bibliographic record
Abstract
The COVID-19 pandemic has caused the biggest and most widespread disruption onto global supply chain networks in recent memory.Although hazards and natural disasters occur more frequently, an unparalleled demand for supply chain networks to reconsider their resilience has been observed.Understanding how global companies manage their supply chain disruptions will help other companies adapt their own responses.We carried out a study on 17 systematic literature reviews (SLR) that portrayed the state of the supply chain resilience (SCR) in the last 10 years and present the authors' synthetized definitions, their associated elements and characteristics.The purpose of this paper is to draw an insight on how the definitions of the concept of resilience in the supply chain have evolved over time from a scientific community perspective, through answering our focused research questions, and providing direction for future research.
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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 | 0.056 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.046 | 0.042 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".