Bibliographic record
Abstract
In recent years, negativity has become a dominant theme in the political campaign. However, there are no comprehensive studies to measure the amount of negativity and to examine how parties and candidates adopt these negative strategies, particularly in the Canadian context. Although some studies have focused on a particular aspect of negative campaigning in a Canadian election, the question remains of how and to what extent parties adopt negative strategies in an election. In this thesis, I have collected and analyzed parties’ press releases in the 2015 federal election to examine and explain negativity in parties’ political campaigns. I have tested my results according to five primary theories of negative campaigning, including competitive positioning, ideological proximity, party organization, coalition or minority effect, and negative personalization, to see if these theories apply in the Canadian context. My results indicate that the 2015 federal campaign was a highly negative one, and most of the negative attacks have been directed towards the leader of the Conservative Party, Stephen Harper, while the Conservative Party published the least amount of negative attacks during the campaign. I also found that the Liberal Party has published the most negative statements during the campaign. My results also show that one of the influential factors in shaping parties’ negative campaign strategies is the other parties’ status in public opinion polls, particularly the federal voting intention factor. Although the results show that most of the attacks in the 2015 campaign targeted leaders of parties, I did not find enough support in my models to verify the negative personalization theory. The overall findings of this thesis show that Canadian elections are moving toward a presidential-style campaign, similar to the United States, by becoming more negative and more personalized, which can have significant implications for Canadian democracy.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".