Bibliographic record
Abstract
This book came about because, to anyone paying attention to the news, it is clear that Canadian politics and governance have been changed by an unrelenting mentality among political elites of non-stop political marketing, communication, and strategic thinking.It is the third in a series looking at the inner workings of Canadian politics and governance.The first, Political Marketing in Canada (UBC Press, 2012), examined the ways that Canadian democracy is changing as public sector elites use market intelligence and marketing tactics.The second, Political Communication in Canada: Meet the Press and Tweet the Rest (UBC Press, 2014), explored the ways that political communication and political behaviour are profoundly changing.A fourth volume is in development to examine the behaviour of Canadian political elites themselves in the digital age.All of this is part of the UBC Press series Communication, Strategy, and Politics, for which a description can be found on an earlier page.The open-access compilation Canadian Election Analysis 2015: Communication, Strategy, and Democracy (available for free public download at www.ubcpress.ca/CanadianElectionAnalysis2015) is also affiliated with the series, as are a number of other publications.The editors would like to thank Tom Flanagan for authoring the foreword.Flanagan is a prolific University of Calgary academic who has published about permanent campaigning in Canada, often drawing on his experience running a number of
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.381 | 0.275 |
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".