Investigating Rural Logistics and Transportation through the Lens of Quadruple Bottom Line Sustainability
Bibliographic record
Abstract
Background: An alternative to unsustainable urban developments, rural living is on the rise, but it already has its challenges. To that end, rural logistics and transportation (RLT) calls for a comprehensive analysis of its context, especially in a climate-changed and socially turbulent world. Unlike urban logistics, there is limited focus on RLT in academic literature. However, rural areas’ lack of transportation and limited logistics operations negatively affect rural residents’ daily lives, especially socially disadvantaged groups such as older people, children, women, and low-income households. Methods: This study first identifies the key literature on RLT and sustainability using a systematic literature review. Then, it synthesizes from the extant literature the challenges in RLT and proposed solutions to understand how to improve accessibility and address some barriers to implementation, all through the perspective of quadruple bottom line (QBL) sustainability pillars. Results: The lack of opportunities presented to rural residents due to limited RLT leads to inequality between rural and urban populations, requiring academic attention. Moreover, despite the growing emphasis on sustainability in academic literature, there is a noticeable lack of attention to sustainability in RLT. Conclusions: This study leads policymakers toward a better understanding of rural communities’ complexities, directs practitioners to adopt the QBL perspective in decision-making, and aims to stipulate innovative RLT topics for further research for academicians.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".