Bibliographic record
Abstract
This book is a follow-up, of sorts, to Political Marketing in Canada (Marland, Giasson, and Lees-Marshment 2012), which argued that the main reason that Canadian political elites use market intelligence such as opinion polling and focus group data is to inform their communication decisions.In Political Communication in Canada, we explore ways that changes in communication technology and media behaviour are affecting Canadian politics.This includes the communication between political parties, politicians, public servants, interest groups, the media, and Canadian citizens in the digital age.In the preface to Political Marketing in Canada, Conservative Party marketer Patrick Muttart is noted as emphasizing that political parties manage earned media, paid media, direct voter contact, local matters, and social media simultaneously during an election campaign.This requires the use of centralizing tools.The Conservatives use their Constituency Information Man age ment System (CIMS) computer software to organize information about electors, while within the Government of Canada they introduced the Mes sage Event Proposal (MEP) to coordinate thematic messaging.As the news cycle speeds up, and as the line between an election campaign and inter-election period blurs, this media management is now constant.Brad Lavigne, who was the New Democratic Party's director of strategic communications during Jack Layton's tenure and the NDP campaign director in 2011, advises that communication management has taken on greater importance in politics for the following reasons:
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.494 | 0.284 |
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".