Bibliographic record
Abstract
The complexity and interconnectedness of current times demand the designing of policies, systems, and services—addressing sustainability challenges and enabling actionable changes for the future—using a systems approach. This paper proposes a conceptual model for applying a systems approach to design interventions, including policy development, to create a sustainable, actionable future. The conceptual model builds on the core features of systems thinking to understand the existing context and analyse gaps for sustainable, systemic interventions through design interventions. Systems mapping and modelling play a key role in understanding systems dynamics and feedback loops, identifying gaps, analysing principles of balance and inter-relating perspectives. Design projects carried out in this area and written as case studies keeping these aspects in mind are further reflected upon in terms of possible design directions and analysed. The analysis and reflections on the practice form the basis for constructing a framework connecting design approaches, systems thinking and sustainability factors. The conceptual model is expected to help designers navigate the complexities of sustainability challenges and provide a roadmap to apply a systems approach to developing design interventions: policies, systems, and service models for a sustainable future. It can also help establish feedback mechanisms to monitor policy outcomes and enable policymakers to respond to changing circumstances through an iterative process. This systemic, generative, creative approach is expected to help build a resilient, flexible, hopeful future.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".