Perspectives on confronting issues of scale in systems modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Issues of scale pervade every aspect of socio-environmental systems (SES) modeling. They can stem from the context of both the modeling process, and the purpose of the integrated model. A webinar hosted by the National Socio-Environmental Synthesis Center (SESYNC), The Integrated Assessment Society (TIAS) and the journal Socio-Environmental Systems Modelling (SESMO) explored how model stakeholders can address issues of scale. Four key considerations were raised: (1) being aware of our influence on the modeling pathway, and developing a shared language to overcome cross-disciplinary communication barriers; (2) that localized effects may aggregate to influence behavior at larger scales, necessitating the consideration of multiple scales; (3) that these effects are “patterns” that can be elicited to capture understanding of a system (of systems); and (4) recognition that the scales must be relevant to the involved stakeholders and decision makers. Key references in these four areas of consideration are presented to complement the discussion of confronting scale as a grand challenge in socio-environmental modeling. By considering these aspects within the integrated modeling process, we are better able to confront the issues of scale in socio-environmental modeling.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it