The Swarm City - Characteristics of the Swarm Intelligence System in the Traditional Arab City
Bibliographic record
Abstract
Specialized architectural studies based on the swarm concept have emerged for nearly two decades. The formation formula based on swarm intelligence is beginning to emerge as a methodological and conceptual basis for a range of generative architectural design strategies. The intelligence of the swarm and the logic on which the swarm system is based can be seen in traditional urban formations, where traditional cities follow models of sustainable natural processes, which best match the simple rules of the swarm system. Accordingly, the research problem was recognized as “the lack of a cognitive background for the characteristics of the swarm’s intelligence system and its function in the structure of the traditional Arab city fabric”. Therefore, to solve this problem, the significance of the natural systems and their impact on the sustainability of cities was determined, and the definition of the swarm city, and the theoretical base that supports the features of the swarm intelligence system was developed based on its items extracted in the analysis of the traditional Arab city and the structure of its historical fabric. The architectural dependence of swarm intelligence is not an attempt to mimic nature only but to examine the generative potential of swarm logic and draw emerging behavior in generating complex attributes in form, organization, pattern, or structure. Traditional Arab cities, in the structure of their historical fabric, follow the typical characteristics of a swarm intelligence system, in which systems of city formation are embodied according to characteristics of multiple agents, emerging collective behaviors, and highly coordinated self-organization.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".