Distributed Leadership Theory in Creating Capabilities and Learning Outcomes in Higher Education: An Analysis of Online Leadership
Bibliographic record
Abstract
Online leadership of higher education institutions (HEIs) is conducted on Twitter. By highlighting the existing patterns of interactions, distributed leadership (DL) is not only eminent in its simplest form, but collegiality and autonomy can readily be realized. New knowledge, based on the tweets and collegial online interactions from the 14,183 tweets sent by the HEIs in the US, UK, Canada, and South Korea, not only promotes the HEIs' unique online persona, but captures the very essence of leadership under investigation. Distinct leadership styles are separated into any one of the twenty-one administrative activities, which I constructed. Further, the learning opportunities and outcomes, associated with either managerial and non-managerial functions, culminate as the distinguishing features of online leadership. Contextual analysis has been applied to navigate the open conversations and the interactions taking place to categorize and measure the impact of the leadership in the ongoing practice of DL as a demonstrative theory. Long-term instruction in all its administrative roles is favored as the means for providing a new form of education—tweet by tweet. How the learning objectives are being advanced in the tweets themselves governs not only the resulting leadership style, but predicts the learning process in HE.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".