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Record W4410923941 · doi:10.1007/978-3-031-86889-4_12

Governance, Management and the Academic Profession: Themes and Concluding Observations

2025· book-chapter· en· W4410923941 on OpenAlexaff
Glen A. Jones, Liudvika Leišytė, Mónica Marquina

Bibliographic record

Venue˜The œchanging academy · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsCorporate governancePolitical sciencePsychologyPsychoanalysisManagementSociologyEconomics

Abstract

fetched live from OpenAlex

Abstract Drawing on data from the Academic Profession in the Knowledge-based Society project, an international collaborative research study involving the administration of a common survey to faculty in more than twenty countries, this volume explored important issues of governance and management in relation to, and frequently from the perspective of, the academic profession. This chapter provides an overview and analysis of the key findings emerging from this collection of original empirical studies. These studies reinforce the importance of recognizing the complex histories and national contexts that underscore system and institutional reforms and illuminate key nuances within the complex relationships between decision-processes and the realities and perceptions of academics. Increasing managerialism is a common theme, and contributors analyze the multi-faceted implications of this shift for universities and the academic profession, including issues of gender, shifts in faculty perceptions of influence on academic decisions, and the importance of communication and institutional leadership.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0040.010
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.327
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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