Assembling and Governing the Higher Education Institution
Bibliographic record
Abstract
This book emphasizes the inherently democratic nature of education; from those who practice in higher education institutions and are involved in decision-making, to those questioning the methods of reform processes in those institutions. As they are faced with increasing pressures to restructure and change their organizations in line with global institutional demands the foundations upon which their leadership and governance are based are called into question. This book takes a critical approach to understanding higher education leadership and governance. The overarching questions asked in this book are: how has higher education come to be assembled in contemporary governance practices within the context of global demands for reform and how are issues of justice being taken up as part of and in resistance to this assemblage? Lynette Shultz is Associate Dean, International, and Director of the Centre for Global Citizenship Education and Research in the Faculty of Education at the University of Alberta, Canada. Her key research is focused on democracy, social justice, and global citizenship education, and the internationalization of higher education. Melody Viczko is Assistant Professor of Critical Policy, Equity and Leadership Studies at Western University, Canada. Her research engages a relational approach to educational policy analysis, with a fascination for how actors assemble around policies and how these assemblages influence democratic governance practices in education. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".