Strange Attractors and Human Interaction: Leading Complex Organizations through the Use of Metaphors
Bibliographic record
Abstract
This article is intended to explore the theoretical background behind complexity science in management and leadership and provide ways to approach educational leadership research through the use of strange attractor metaphors. Historical and contemporary leadership strategies have incorporated modernistic models that sometimes perpetuate problematic aspects of educational management rather than provide progressive solutions. Several leadership researchers have shown, however, there is tremendous potential for the emergent properties of complexity theory in organizational dynamics. The recognition and utilization of strange attractors as metaphorical constructs of chaos theory also provide us with an elaboration of teaching and educational leadership theory. Strange attractors seem to exist metaphorically in many aspects of the organizational dynamics of our educational institutions. The use of metaphors in lived experience is described, the scientific background behind strange attractors is introduced, and connections are made between strange attractors and human interaction. Strange attractors are then metaphorically described in organizational settings as shared vision, team processes, and information flows used as positive feedback mechanisms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".