MétaCan
Menu
Back to cohort
Record W2513811234 · doi:10.30828/real/2016.1.3

Higher Education Administration, and Leadership: Current Assumptions, Responsibilities, and Considerations

2016· article· en· W2513811234 on OpenAlexaff
Charles F. Webber

Bibliographic record

VenueResearch in Educational Administration & Leadership · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsMount Royal University
Fundersnot available
KeywordsUnintended consequencesPublic relationsAdministration (probate law)CurriculumHigher educationPolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

This article profiles the evolving role of educational administrators and leaders in higher education. Four guiding assumptions for leaders are presented related to social impact, community engagement, labor market success, and institutional stability. Then, seven key administration and leadership responsibilities are described. They include planning, academic entrepreneurship, data-driven decision making, revenue generation, creating professional and academic pathways for learners, curriculum development, and business development and marketing. This is followed by a set of pragmatic considerations that higher education administrators and leaders may consider in their professional practices. The considerations provide a framework for interrogating leadership assumptions and responsibilities, a framework that can be applied to analyze additional responsibilities as they emerge in relation to the assumptions that accompany them. The considerations pose intended and unintended possibilities for leaders to use to inform decision making, maintain principled leadership practices, and to challenge unexamined beliefs and values.

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)0.053
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.043
Scholarly communication0.0200.020
Open science0.0030.007
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.427
GPT teacher head0.476
Teacher spread0.049 · 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 designTheoretical or conceptual
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

Citations17
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueResearch in Educational Administration & LeadershipSame topicHigher Education Governance and DevelopmentFrench-language works237,207