Reflecting Emerging Digital Technologies in Leadership Models
Bibliographic record
Abstract
In this chapter, Smith and Cockburn reaffirm the claim that they made in a previous book (Smith & Cockburn, 2013), namely that today's global business environments are characterized by volatility, uncertainty, complexity, and ambiguity, and that leaders must focus less on developing behavioral competencies and more on complex thinking abilities and mindsets. In so doing, leaders must be familiar with emerging digital technologies, their benefits and drawbacks, and utilize these technologies in their practice as appropriate. In their previous book (Smith & Cockburn, 2013), the authors defined flexible and dynamic leadership models that assure successful leadership in the above turbulent contexts, and also described learning related processes that are essential to mastering the ability to learn and adapt at rates consistent with the business complexity leaders face. In this chapter, the authors extend their previous research (Smith & Cockburn, 2013), review newly emerging elements of social digital connectivity that are contributing to global business complexity, and explain how these elements may be applied by leaders to augment the power of the recommended dynamic leadership models.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".