Identify Emergence in Social Systems Using an Extended E-CARGO Model
Bibliographic record
Abstract
How to identify an emergent property in a social system is a challenging topic. Social system modeling is an effective way to understand and explain the emergence phenomena. A reversed process of Role-Based Collaboration (RBC) is proposed for emergence studies. The Environments–Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is the fundamental model of RBC. A well extended E-CARGO (EE-CARGO) model is proposed to modeling a social system with emergent properties. The system is abstracted into agent-role level, roles are modeled as a tree structure and role emergence is identified and analyzed at multiple scales. Then we derive that the masses are primary, and the individual agent is secondary to understanding the emergence forming. A case study is accomplished to verify the effectiveness of proposed approach (i.e., the reversed RBC process and EE-CARGO model). The results are revealed that the role emergence fits a cubic curve estimation by time and at a moderate scale, the role emergence happens easily. Role performances at a small scale affect super-role emergence at a large scale statistically, and vice versa. The proposed approach can be applied to investigate emergence in a variety of areas, such as social evolution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".