The Social Electorate: Explaining Politics Through Lifestyle in Canada
Bibliographic record
Abstract
Since the advent of political polling in the 1940s in the United States, political science research has shown that certain key characteristics – such as social class, religion, or place of residence – have had a strong effect on voting choice. These structural factors traditionally played a crucial role in predicting voters’ behavior. However, as modern societies have become increasingly fragmented in terms of cultural and social dynamics, the predictive power of these conventional cleavages has shown a decline. This does not imply that these factors are no longer relevant, but rather that the political landscape has become more intricate – that is, we observe a growing heterogeneity in terms of political preferences among these traditional groups. Relying on the extensive and granular data of Datagotchi, this thesis examines the relevance of an understudied and often overlooked concept in political science: lifestyle. Because it intersects with both individual and contextual characteristics, lifestyle allows to consider both an individual's personality and interests within a given social and cultural context into the identification of socially and politically significant subgroups. Using Canada as a case of study, this thesis therefore provides a first comprehensive and empirical application of how lifestyle can be used in electoral behaviour research. The broad argument of this thesis is that people’s lifestyle is politically meaningful. The way people act, behave or dress speaks about who they are. It also impacts the way they are politically perceived. This thesis has far-reaching implications. The relevance of lifestyle challenges the individualization thesis, which suggests an increasing unpredictability in politics. Indeed, despite the erosion of old long-term influences on political choices, new social groups are still politically significant. The degree to which lifestyle predicts vote choice also directly impacts the strategic options available to political parties beyond mobilizing their existing electoral base. More broadly, this thesis also delves into certain ethical considerations associated with the richness and the value of seemingly trivial, non-political data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".