Bibliographic record
Abstract
Brian S. Mitchell defines the state of the art of research on graduate education and identifies some of the key changes, needs, and issues facing graduate education and research in the 21 st century.Mitchell explains that the impetus for this book stemmed from the need to rethink graduate education through "more timely data, longitudinal studies across a wider array of institution types, and theoretically sound and robust models on more specific research questions that consider multiple factors and their interactions" (p.xvii).Indeed, Mitchell's central message in the book is that efforts to improve, innovate, and maintain excellence in graduate education are not only urgently needed, but also dependant on sound evidence.In addition to introductory and concluding chapters, this book consists of three chapters that focus on distinct facets of graduate education research: The theoretical nature of teaching and learning at the graduate level, the implementation of pedagogical practices, and the development and assessment of graduate education.While this book may be of interest to a wide audience of researchers, educators, and administrators Book Review: Tavares xvi Canadian Journal of Education / Revue canadienne de l'éducation 46:1 (2023) www.cje-rce.caworking in graduate education, the core chapters tackle each of the three facets rather discretely, thereby delineating more fixed boundaries between these groups of readers, and Mitchell presents a convincing argument for organising the book in such a way.In the Introduction, Mitchell builds a concise, yet thorough context for his research agenda.The topics within the three core chapters are structured sequentiallythat is, in addition to being all thematically interconnected, they build upon one another in a consecutive, logical manner.By designing the chapters in such a style, Mitchell
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".