Bibliographic record
Abstract
The production of a book identifying, characterizing, and analyzing trends in political communication and behaviour in Canada would have been impossible without the innovative and thoughtful research of dedicated scholars from a wide range of disciplines, including political science, political management, communication, sociology, and journalism.The editors would first like to thank all of the contributors to this book not only for sharing their work but also for exploring emerging areas of research likely to foster fascinating studies over the next decade.We would also like to acknowledge the support of federal and provincial funding agencies, including the Fonds de recherche société et culture and the Social Sciences and Humanities Research Council, as well as numerous universities for making this research possible.In particular, we would like to thank the Département de lettres et communication sociale as well as the Décanat de la recherche et de la création of the Université du Québec à Trois-Rivières and its dean, Sébastien Charles, for their financial support for production of the book.Our thanks are also extended to the Department of Communication Studies of Emerson College and Acadia University for their institutional and, in many instances, personal support.We gratefully acknowledge the unparalleled assistance and support provided by the editors of the book series Communication, Strategy, and Politics with UBC Press, Alex Marland from Memorial University and Thierry Giasson from Université Laval.By guiding our work and providing helpful feedback, this book is undoubtedly better because of them, and it complements previous works published in the series.The guidance and
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.279 | 0.179 |
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".