Collaborative Educational Leadership: The Emergence of Human Interactional Sense-Making Process as a Complex System
Bibliographic record
Abstract
The article aims at explicating the emergence of human interactional sense-making process within educational leadership as a complex system.The kind of leadership is understood as a holistic entity called collaborative leadership.There, sense-making emerges across interdependent domains, called attributes of collaborative leadership.The attributes give rise to the complex system.They are suggested to be the very agents, i.e. both the source and the outcome of the synergetic sense-making process.Hence, the agents are not the single persons involved who, however, supply the collective attributes that are modified through human interaction in a holistic way.For studying the emergence process in reality, a long-term development process within an educational executive team was exploited.The team aimed at co-creating novel leadership thinking and working practices for its new unit after a merger of separated schools.The emergent sense-making process was examined through such agent-attributes that were identified as attractors within the complex system.Moreover, it is argued that illuminating the complex system of collaborative leadership, this can help other leadership teams to better understand their own sense-making processes in the increasingly complex settings of today.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".