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Record W1856947370 · doi:10.47678/cjhe.v32i1.183404

Do University Presidents Make a Difference? A Strategic Leadership Theory of University Retrenchment

2002· article· en· W1856947370 on OpenAlexaffvenueabout
Art Budros

Bibliographic record

VenueCanadian Journal of Higher Education · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRetrenchmentAgency (philosophy)Public relationsHigher educationPerceptionPolitical scienceEconomicsAccountingSociologyManagementPublic administrationPsychologyLaw

Abstract

fetched live from OpenAlex

Although the general consensus is that university presidents do not make a difference, most research on this issue is atheoretical and perceptual in nature. Consequently, I draw on strategic leadership theory to examine whether observable experiences of presidents affected the adoption of retrenchment (early faculty retirement) plans among Ontario universities from 1989-90 to 1997-98. In doing so, I also advance strategic leadership theory by introducing novel measures of leaders' observable experiences and by testing their impact on an outcome (retrenchment) in a noncorporate setting. Since early faculty retirement plans have spread across universities, and since evidence indicates that they combine financial benefits and academic costs, their adoption represents a key structural change begging for explanation. Although the literature attributes the adoptions to uncontrollable forces, I report that presidents play a major role in the adoptions. Those who lack seats on corporate boards, who have non-business or non-economics backgrounds, who are male, and who are recruited externally adopt the plans at lower rates than their respective counterparts. The results portray presidents as exercising circumscribed agency, strategizing on the basis of their personal and professional experiences and making a difference even under inhospitable circumstances.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.111
GPT teacher head0.273
Teacher spread0.162 · 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.

Study designObservational
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

Citations11
Published2002
Admission routes3
Has abstractyes

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