Bibliographic record
Abstract
For operationalising design for regeneration, we need to take decisions across scales of governance and space and adopt practices of continuously zooming in on details and then zooming out to see the bigger picture, which are processes in dynamic relation. The poster offers a spiral approach to relating circularities on eight nested spatial and governance scales, from green chemistry to transnational cooperation (Luthe, Fitzpatrick & Wahl, 2020). Planning and design decisions on one scale have a direct or indirect impact on all scales; thus, governance must better evaluate cross-scalar systemic effects. This conceptual framework is partly based on Wahl (2006) and is here presented in the form of a novel contribution to designing resilient regenerative systems. The mathematical foundation is the Fibonacci spiral: a common functional pattern occurring throughout nature, it provides an analogy for better communicating the scalar processes of designing and governing resilient regenerative systems (Drozdyuk and Drozdyuk, 2010; Bistagnino, 2018). A widely supported goal is to transform our economies and societies as interconnected systems with locally adapted solutions corresponding to global dynamics. This cross-scale governance approach applies the design of resilient regenerative systems to deal with “wicked problems”, such as how evidence-based solutions are rarely directly translated into decision-making. The inherent complexities in such problem-solving require incubating more effective transformational action (Elzen & Wieczorek, 2005; Wahl & Baxtor, 2008). Generating clear visualisation strategies is critical to communicate and encouraging evolving debates on the regenerative design of resource production and distribution (Fazey et al., 2020). Building upon published work (Wahl, 2016), the model offers a spiral approach to relating nested spatial and governance scales, from green chemistry to transnationalities, with their inherent circularities, identifies planning and design decisions that have direct or indirect impacts on all scales; thus, governance must better evaluate these cross-scalar systemic effects (Wahl, 2006).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".