Focus on global–local–global analysis of sustainability
Bibliographic record
Abstract
Abstract This special issue is the outcome of a workshop held at Purdue University in April 2022. It comprises thematic syntheses of five overarching dimensions of the Global-to-Local-to-Global (GLG) challenge to ensuring the long-term sustainability of land and water resources. These thematic dimensions include: climate change, ecosystems and biodiversity, governance, water resources and cyberinfrastructure. In addition, there are eight applications of GLG analysis to specific land and water sustainability challenges, ranging from environmental stress in the Amazon River Basin to groundwater depletion in the United States. Based on these papers, we conclude that, without fine-scale, local analysis, interventions focusing on land and water sustainability will likely be misguided. But formulating such policies without the broader, national/global context is also problematic – both from the point of view of the global drivers of local sustainability stresses, as well as to capture unanticipated spillovers. In addition, because local and global systems are connected to – and mediated by – meso-scale processes, accounting for key meso-scale phenomena, such as labor market functioning, is critical for characterizing GLG interactions. We also conclude that there is great scope for increasing the complexity of GLG analysis in future work. However, this carries significant risks. Increased complexity can outstrip data and modeling capabilities, slow down research, make results more difficult to understand and interpret, and complicate effective communication with decision-makers and other users of the analyses. We believe that research guidance regarding appropriate complexity is a high priority in the emerging field of Global-Local-Global analysis of sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".