A Functional Analysis of the 2011 English Language Canadian Prime Minister Debate
Bibliographic record
Abstract
In March of 2011, Conservative Canadian Prime Minister Stephen Harper lost a vote of no confidence in Parliament, which triggered an election in May and two debates in April. Three challengers also participated in the debates: Michael Ignatieff (Liberal), Jack Layton (New Democratic Party), and Giles Duceppe (Bloc Quebecois). This study applied the Functional Theory of Political Campaign Discourse to the English language debate. Attacks and acclaims (which occurred at about the same frequency) were more common than defenses. However, incumbent Prime Minister Harper acclaimed more than he attacked – and more than the three challengers. The challengers attacked more than they acclaimed – and more than the incumbent. This contrast was particularly acute when the candidates discussed past deeds (record in office). Each of the four candidates discussed Harper’s record more than any other candidate’s record and, of course, Harper acclaimed when he discussed his record whereas the challengers attack when discussing Harper’s record. These four candidates discussed policy more than character. When discussing general goals and ideals, they acclaimed more than they attacked. These results are compared with studies of political leaders debates in other countries and elections.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".