Where’s the data? Challenges in characterizing the sustainability of socioecological systems
Bibliographic record
Abstract
Abstract In an era of great need regarding nexus-type research and assessments, this study investigates the challenges of missing data encountered by ten ( N = 10) societal metabolism scholars who use MuSIASEM (Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism)—a rigorous nexus-type research method. We explore how these scholars tried to overcome data challenges, their reflections on data availability, and how governments and other institutions might benefit if they had the missing data the scholars sought. Additionally, the study delves into the scholars’ interpretations of why the data is missing in the first place. Using a grounded approach, this qualitative study examines interview texts and reveals a range of issues that scholars faced, including an excess of aggregated data—a lack of disaggregated data—problems with data categorization, and other issues. On the whole, respondents’ reflections align with the foundational arguments of MuSIASEM’s developers, suggesting that more comprehensive, granular, and therefore effective approaches are needed to address socioecological challenges, sustainability issues, and net-zero goals. Respondents also noted that much of the missing data could be attributed to a dominance of economic logics and conceptual frames that often obfuscate the material and biophysical foundations of those economic systems themselves. This study advocates for revisiting and enhancing those conceptual frameworks that shape “what data is collected” and how it is made available in order to enhance analyses and broaden collective deliberations toward more informed sustainability and related policy decisions.
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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 | 0.268 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.019 | 0.039 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".