Personalized Politics as a Communication Strategy: Which Factors Influence the Personalism Employed by Canadian Political Parties?
Bibliographic record
Abstract
There is a prevailing notion that politics is becoming more personalized, marked by a shift in power from political groups, such as parties, to individual actors. This increasing influence and centrality of individuals, at the expense of political parties, is expected to be evident in political institutions, news coverage, communications controlled by political actors, and the behaviors of politicians and voters. The waning influence of political parties, the growing role of media in shaping politics, and the rising emphasis on individualism in society are all seen as contributing factors to this phenomenon in political systems worldwide. However, despite these theoretical expectations, empirical evidence confirming such personalization, particularly in the Canadian context, remains scarce. Some experts suggest examining ‘personalism,’ which refers to the current state of personalized politics, regardless of broader longitudinal shifts, and shift from the broader macro-causes to a strategic conceptualization of personalized politics, being part of a campaign strategy aimed at making electoral gains. This research takes this approach by investigating how situational and meso-level factors impact the strategic choices of political parties and leaders to personalize or not their communications. It tests the impact of various stages within an electoral cycle and of the coronavirus crisis, as well as political parties and party leader characteristics on seen levels of personalism. This research conducts a content analysis on a comprehensive and original dataset of party and party leaders' communications, totaling 18,975 Facebook posts and 1,286 party website communications. It encompasses the most competitive political parties in all 10 provinces, as well as at the national level, including 29 political parties under the leadership of 49 party leaders. The findings illustrate that political parties and leaders take various strategic factors into account and adjust their strategies accordingly, including the decision of whether to engage in personalism. Consequently, these results indicate a promising avenue for future research aimed at enhancing our understanding of how situational factors influence the personalized character of politics, regardless of long-term trends.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".