Bibliographic record
Abstract
We present a conceptual framework that explores some of the forces creating innovation and novelty in complex systems. Understanding the sources of variability and novelty may help us better understand complex systems. Understanding complex phenomena such as invasions, migration, and nomadism may provide insight into the structure of ecosystems and other complex systems, and aid our attempts to cope with and mitigate these phenomena, in the case of invasions, and better understand and or predict them. Our model is broadly applicable to ecological theory, including community ecology, resilience, restoration, and policy. Characterizing the link between landscape change and the composition of species communities may help policymakers in their decision-making processes. Understanding how variability is related to system structure, and how that generates novelty, may help us understand how resilience is generated. We suggest that there are three primary opportunities for the generation of novelty into complex systems. These sources of novelty are inherent in the cross-scale structure of complex systems, and are critical for creating adaptive capacity. Novelty originates from the inherent variability present in cross scale structures, within scale reorganization associated with adaptive cycles, and whole-scale transformations resulting from regime shifts. Although speculative, our ideas are grounded in research and observation, and they may provide insight into the evolution of complex systems.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".