Bibliographic record
Abstract
With an unprecedently aging population and the abolition of mandatory retirement in many countries, management educators are remaining in their jobs longer than ever before; thus, it has never been more important to ask the question of: how can management educators remain effective and engaged while avoiding burnout throughout a career in the academy? The issue of aging well in management education is relatively under-acknowledged in the literature and we sought to move this topic into focus for higher education institutions and management educators. The interview we present focuses on the experiences of an accomplished management scholar and educator: Professor Emeritus and Full Professor, Gene Deszca. Dr. Deszca aged well as a management educator during his 37-year career at his institution until his retirement at the age of 69 and a half. The major themes from the interview suggest the benefits of interactions and relationships, autonomy, institutional support, and a willingness and ability to change. Based on these major themes, we provide implications for higher education institutions and management educators. It is our hope that management educators will engage with this interview and reflect on their own experiences while considering how they can age well throughout their career in the academy.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".