Leveraging Partnerships in Assessment as a Pillar of Leadership in VUCA Contexts
Bibliographic record
Abstract
This a conceptual chapter that seeks to create a new framework for the different forms of partnership for assessment. This chapter will explore the necessity and advantages of a collaborative approach to leadership of assessment in the context of rapid advancements in artificial intelligence (AI). As AI technology continues to evolve, the dynamics of organizational leadership are undergoing a fundamental transformation. Traditional hierarchical leadership models are becoming increasingly inadequate in addressing the complexities and ethical considerations associated with AI integration. This chapter will argue that a collaborative leadership framework, characterized by shared decision-making with students, inclusivity, and interdisciplinary cooperation, is essential for effectively navigating the opportunities and challenges posed by AI. The chapter contains detailed examples of organizations that have successfully adopted collaborative leadership models, lessons learned from these case studies, and an analysis of how collaborative leadership could contribute to the success of AI initiatives in higher education organizations. This chapter is valuable to a wide range of readers, including but not limited to organizational leaders seeking to understand how AI impacts leadership, scholars and students of leadership studies, AI ethics, and organizational behavior, as well as policymakers interested in the ethical implications of AI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".