Leadership Decision-Making and Insights in Higher Education: Making Better Decisions and Making Decisions Better
Bibliographic record
Abstract
This article proposes a new framework for Principals called the Objective Knowledge Growth Framework (OKGF) that is designed to help them make more effective decisions in resolving problems of practice.It also provides a structure to help principals break away from education systems that impose inductive practices, as it provides a framework for supporting the decision-making processes of others as well as enabling rationality in their own practice.The use of the OKGF framework is designed to enhance individual reflection which, in turn, is multiplied by others through dialogue, interaction and engagement with others.Through interaction, dialogue and engagement, greater and improved insights into decisions are more likely to occur than if knowledge and information continues to be compartmentalized within schools and, consequently, performance assessments are more likely to be enhanced.Not only does the OKGF have the capacity to improve principals' performance, it also provides a framework by which principals may maximize student success.
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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 | 0.046 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".