Towards Understanding and Sustaining Natural Resource Systems through the Systems Perspective: A Systematic Evaluation
Bibliographic record
Abstract
A bibliometric and network analysis was performed to explore global research publication trends and to investigate relevant policy recommendations in the field of sustainability of natural resources, system dynamics, and systems thinking, to solve water resources issues and enhance water resource management. Overall, 1674 academic research articles data were generated from the Web of Science and Scopus databases, from 1981 to 2019. The findings of this study revealed that system dynamics and systems thinking research has significantly increased over the last decade (from 40 to 250 articles). Countries such as the USA (20%), China (18%), the United Kingdom (5%), Canada, Iran, Australia, and India (4% each) have the most publications and strongest collaborative networks. Sterman (2000) and Forrester (1961) had the most co-cited research while Zhang X had the highest citations, respectively. Results also showed that system theory which includes systems thinking and system dynamics were the most used keywords. The Journal of Cleaner Production was found to have published the highest number of systems thinking and system dynamics related studies, perhaps due to scope relevance. Despite the exponential rise in natural resource sustainability research globally, the result of this study shows that developing countries especially in Africa have low numbers of research publications in the field. Thus, the result of this study serves as a signal for policymakers to increase attention on research publications that could enhance natural resource sustainability, particularly in less developed countries in Africa where the application of systems thinking to natural resource management is limited.
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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 | 0.089 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.088 | 0.066 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".