Generative IA and complexity – Towards a new paradigm in regenerative digital design
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
The synthesis of artificial intelligence, deep learning and parametric design in regenerative digital design can significantly reshape the pre-design phase in climate scenarios. By formulating a new workflow linking computational processes with human-centred design, it is possible to realise a more adaptive approach to environmental design that anticipates the complexities of our built environment and fosters responsive and resilient collective creativity. Starting from the abstract and introduction, a focus is proposed to investigate the complexity and emerging field of regenerative digital design, particularly in climate scenarios. The basic premise is that AI’s deep learning and natural language processing capabilities can go beyond simple visual outcomes to address nuanced and multifaceted design challenges. Article info Received: 14/10/2024; Revised: 18/10/2024; Accepted: 20/10/2024
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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