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Record W2526855755 · doi:10.1057/978-1-137-52261-0

Assembling and Governing the Higher Education Institution

2016· book· en· W2526855755 on OpenAlexaffabout
Lynette Shultz, Melody Viczko

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsWestern UniversityUniversity of Alberta
Fundersnot available
KeywordsInstitutionPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

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. .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.296
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations11
Published2016
Admission routes2
Has abstractyes

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