Examining What Institutional Structures and Forms of Leadership Support the Growth and Development of Non-Academic Professional Staff in Universities
Bibliographic record
Abstract
The study of career development pathways and support for the population of university employees who do not hold an academic appointment is lacking. Many departments across one eastern Ontario institution are coming to rely on non-academic professionals to help drive strategic objectives within their respective faculties. The goal of this study is to gain insight into the experiences of the career growth of this group in an effort to understand their career journeys. University environments have become more complex, there are higher expectations from stakeholder groups, and changes to funding models and international competition for the best students and faculty is continually on the rise. All of this change has resulted in increased performance pressure for not just the senior administration, but also for the professional staff who do not hold academic contracts but contribute in many areas to the institution’s success. An increase in postings for non-academic professional administrators to assume leadership roles in program management, strategic projects and many of the more functional areas of the university gives weight to the value that this particular population can bring to the environment. The challenge then becomes how can the institution best support the growth and development of this population, given that the human resource infrastructure currently in place in most higher education institutions has been structured around the support of academic faculty holding many of these leadership roles. Additionally, what changes are needed to meet the needs of this new generation of leaders? Reflecting on my experiences in navigating my own career path is the catalyst for this research project, but with a specific goal of flushing out both the consistencies and differences between staff members across one university in Eastern Ontario in terms of their growth and development.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".