Do University Presidents Make a Difference? A Strategic Leadership Theory of University Retrenchment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".