Bibliographic record
Abstract
This penultimate chapter reflects on an exemplary case of Canadian university leadership drawn from the global cross-cultural leadership artistry research of Professor Nancy J. Adler, Professor of Organizational Behaviour and S. Bronfman Chair in Management, Desautels Faculty of Management, McGill University, Montreal. At the invitation of the editorial team, Professor Adler agreed to have an editorial reflection written about her global leadership contributions deriving from her work at McGill University and around the world, based on insights from her wide-ranging publications. Following searches in Scopus, EBSCO, Google Scholar, Emerald and social media, a selection of articles, several books and films featuring Prof Adler and co-authors were consulted in the writing of the chapter from a corpus of more than 125 articles, three films, ten books and extensive creative works. Professor Adler’s reflective leadership development journal, Leadership Insight, was amongst the works included. A combination of critical and creative thinking is therefore brought together from Professor Adler’s scholarly contributions as a world expert on creative innovations relating to ‘critical being’ in global leadership. The chapter highlights selected aspects of the extensive corpus of insights in her work with colleagues at McGill and elsewhere, and the impact of this work across the fields of global leadership, organisational behaviour and management, the role of women in leadership and the unique contribution of the arts and music to leadership.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".