Complex system models (Systems Thinking, System Dynamics and Agent-based model) for analysing the social aspects of implementing Sponge City adaptation measures in a multi-residential quarter and the implications of policies in the socio-technical system
Bibliographic record
Abstract
Three complex system models for analysing the implementation dynamics of adaptation measures belonging to the Sponge City for a multi-residential quarter: a Causal Loop Diagram as qualitative model (Systems Thinking) - created in Stella Architect a System Dynamics simulation model (quantitative) in a concept stage (running but need to be parametrised in detail) - created in Stella Architect an Agent-based model (quantitative) in a concept stage (basic structure in form of an ODD protocol) In the file "Description of the deposited System Models.docx" you will find more information about the deposited files. The system models deposited here correlate to the publication Schünemann et al. (2024). Modelling the behaviour in socio-technical systems for policy assessment - a comparison of modelling approaches using the example of Sponge City concept implementation. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2024.142722 where they are described in more detail. For any questions about the models, please contact Christoph Schünemann from the Leibniz Institute of Ecological Urban and Regional Development
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.041 |
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".