Socio‐eco‐evolutionary dynamics in cities
Bibliographic record
Abstract
Cities are uniquely complex systems regulated by interactions and feedbacks between nature and human society. Characteristics of human society-including culture, economics, technology and politics-underlie social patterns and activity, creating a heterogeneous environment that can influence and be influenced by both ecological and evolutionary processes. Increasing research on urban ecology and evolutionary biology has coincided with growing interest in eco-evolutionary dynamics, which encompasses the interactions and reciprocal feedbacks between evolution and ecology. Research on both urban evolutionary biology and eco-evolutionary dynamics frequently focuses on contemporary evolution of species that have potentially substantial ecological-and even social-significance. Still, little work fully integrates urban evolutionary biology and eco-evolutionary dynamics, and rarely do researchers in either of these fields fully consider the role of human social patterns and processes. Because cities are fundamentally regulated by human activities, are inherently interconnected and are frequently undergoing social and economic transformation, they represent an opportunity for ecologists and evolutionary biologists to study urban "socio-eco-evolutionary dynamics." Through this new framework, we encourage researchers of urban ecology and evolution to fully integrate human social drivers and feedbacks to increase understanding and conservation of ecosystems, their functions and their contributions to people within and outside cities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".