Can the principle of self-organized gradients be applied for human systems? a case study on rural-urban interactions
Bibliographic record
Abstract
The ecological gradient approach states that self-organized processes produce patterns of concentration profi les that can be distinguished into structural and functional gradients.Throughout the undisturbed development of such systems, certain attributes are regularly optimized.These 'orientors' can be used to characterize the state of open systems.H. Bossel has introduced a set of 'basic orientors', which can be applied as indicators and target functions of any self-organized system.In this paper we combine the gradient approach with the basic orientor concept to test if the principles of self-organization can also be used to describe human entities.The case studies utilized are representing several concentration profi les between urban and rural landscapes in Europe.These spatial gradients, which have been arising from long-term development of cities and their hinterlands, are assigned to the basic orientors' existence, effectiveness, freedom of action, security, adaptability, and coexistence.The results show that in all cases the demanded patterns can be found, thus there are functional parallels between self-organizing processes in ecological and human systems.The basic orientor approach can be used to explain these patterns, i.e. to clarify the utility of the outcome of self-organized processes in nature and society.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".