Exploring the Transition of University Professors to Positions of Additional Responsibility (PAR)
Bibliographic record
Abstract
Effective leadership by academic administrators is an area of significant relevance to self-governing institutions of higher education (Dean et al., 2021; Weaver et al., 2019). Universities’ success depends upon a high degree of competent performance from professors and researchers of varying ranks, particularly those who assume positions of additional responsibility (PAR), including deans, associate deans, department chairs, and program directors. Most universities do not adequately prepare faculty for the challenges of PAR, leading newly appointed academic administrators to experience needless stress and to be less effective administrators, particularly in their early years in PAR (Armstrong & Woloshyn, 2017). Although the higher education literature contains an abundance of research about university presidents, academic deans, and department chairs (White, 2014), studies that focus specifically on these role transitions are sparse. This study explores six faculty members’ transitional experiences to PAR at a southern Ontario university, how they were prepared for their administrative roles, what challenges they faced, and how they were supported in their role. Participants revealed that they were unprepared for their respective PAR, they faced challenges related primarily to “human and people” and budget administration, and the support they received was varied and lacking. Participants also provided recommendations regarding how universities can better support their transitions.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".