Bibliographic record
Abstract
Since Barbara Austin’s 2000 edited collection entitled “Capitalizing Knowledge”, there has been little reflection on the development of management education in Canada. Capitalizing Knowledge covered a range of perspectives on the major influences in management education, including the United States and industry, the development of what is now called the Administrative Sciences Association of Canada (ASAC), the emergence of Canadian business scholarship and sense of legitimacy, and the evolution of business curriculum and factors influencing the expansion of management education and its key drivers. Much has transpired since Austin’s edited collection was published, and this new collection aims to reengage on the conversations that Austin inspired with new contributions. Enlarging our understanding of the history of management education in Canada, this edited collection of essays brings together diverse perspectives, while also introducing new themes in management education including gendered, Indigenous, and Afrocentric perspectives and approaches. Additionally, the authors of this edited collection are community and business school educators, historians, and critical historiographers, thus evoking consideration not just of what constitutes our notion of the history of management education and how and where it is taught, but also how such history is constructed and reproduced and by whom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.363 | 0.138 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".