Advancing the resource nexus concept for research and practice
Bibliographic record
Abstract
Abstract The nexus concept has considerably matured during the past decade. Numerous literature reviews have significantly contributed to taking stock of the advancements in knowledge and tool development to improve science-policy support on highly connected and interdependent resources. However, literature reviews often focus on specific sector-based nexus concepts (such as water-energy-food nexus) and analyses (such as environmental assessment, technical tools, or the management and policy dimension). Therefore, a comprehensive understanding of the actual nexus and the resources it builds upon still needs to be improved. This paper aims to test the validity of the nexus construct for research and practice. Based on a systematic review of reviews, including 62 nexus-related review papers and subsequent consultation of some sixty nexus experts, we suggest a robust but flexible approach to advancing the Resource Nexus for research and practice. In doing so, the knowledge provided by nexus research may provide more substantial support to decision-makers when designing and implementing policies for the sustainable management of environmental resources.
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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 | 0.293 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".