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Record W2612351912 · doi:10.19173/irrodl.v18i3.3294

Adaptive or Transactional Leadership in Current Higher Education: A Brief Comparison

2017· article· en· W2612351912 on OpenAlexaffvenue
Natalie Khan

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsAthabasca University
Fundersnot available
KeywordsTransactional leadershipShared leadershipEducational leadershipDemographicsLeadershipLeadership styleHigher educationNeuroleadershipGlobalizationClass (philosophy)Servant leadershipKnowledge managementPsychologyLeadership studiesMathematics educationPedagogyComputer sciencePublic relationsSociologyPolitical scienceSocial psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Higher education institutions operate in a complex environment that includes influence from external factors, new technologies for teaching and learning, globalization, and changing student demographics to name a few. Maneuvering such complexity and change requires a leadership strategy that is flexible and supportive. This paper reviews two leadership theories in reference to this need: adaptive leadership theory and transactional leadership theory. Three conceptual categories of environmental readiness, leadership complexity, and followers’ motivation are used as points of comparison for each theory. A recommendation is made for leadership strategy in higher education institutions based on this comparison.

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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.451
GPT teacher head0.467
Teacher spread0.016 · 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

Citations81
Published2017
Admission routes2
Has abstractyes

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