Towards Leading Diverse, Smarter and More Adaptable Organizations that Learn
Bibliographic record
Abstract
Leadership is in crisis. Technology has enabled our complex and interconnected world, making it much easier for organizations and entire ecosystems to collaborate—quickly—while older mindsets based on the organization as a machine model are proving to be grossly inadequate. Simultaneously, we have failed to predict and to understand, for example, the cascading financial system failures that threaten lives, institutions, and nations. This chapter takes a complexity thinking perspective to carefully examine specialization, diversity, and organizational change in new ways so that we can extend our leadership thinking about the adaptability of our organizations. Because diversity is a critical condition for complex organizational change, the authors explore diversity from two disciplinary perspectives. First, they take a learning science (education) perspective to find that leaders should consider organizations as emergent collectives that are able to learn and to become capable of “learning ahead” in turbulent contexts. The authors then explore, from an organizational science perspective, how diversity exists as an essential condition for identifying differences and novelties as seeds for innovations (changes) made possible only by collective work attracted to these novelties. Finally, the author presents a framework for understanding and leading and knowing the potentials of diverse, smarter, more adaptive complex organizational ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".