Chaos Design : Designing for Change, with Change
Bibliographic record
Abstract
The graduate project explores chaos theory as a design framework for landscape architecture and other fields of design. It is argued that due to the Anthropocene, the economic, social, and ecological systems are hyper-connected with one another, however it is the economic system that is dictating social welfare and ecological health. This connection of systems create a situation where urban planning and landscape design is highly dependent on monetary goals of profit and cost saving. The overarching inquiry examines chaos theory’s potential to challenge economic-centric systems, to address social needs, and offer to sustainable solutions to climate change. Design interventions will mimic chaotic behaviors in complex adaptive systems. The interventions will take form as a zine booklet for users to manifest their needs in their landscape. Oppenheimer Park, in Vancouver, BC, is selected to experiment on how these interventions will unfold into society. The system encompassing Oppenheimer Park will be modeled as a strange attractor. Design interventions will be used to alter the attractor’s parameters to test how it will behave in a simulation, giving landscape architects a framework on on how their design goals may impact landscape interactions.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".