Voting behaviour and racial identity : exploring the relationship between ethnic identity and its impact on voter turnout in the 2021 Canadian federal elections
Bibliographic record
Abstract
In this thesis, we examined the impact of race on voter turnout in the 2021 Canadian Election using data from the Canadian Election Studies Survey 2021. The analysis was conducted using three models: a bivariate model, a socio-demographic model, and a complete model. Initially, the bivariate model showed a significant negative impact of race on voter turnout, with racialized minorities less likely to vote than White individuals. Adding socio-demographic factors in the second model reduced this effect but it remained significant. However, in the complete model, which included political engagement and socioeconomic status, the impact of race loses statistical significance. This suggests that other factors, such as political interest, partisanship, political knowledge, employment, and income, play a significant role in explaining voter turnout. This study reveals that political knowledge, civic responsibility, political interest, and partisanship significantly impact voter turnout. Higher levels of political knowledge and interest, a strong sense of civic duty, and political party identification positively correlate with increased participation. Economic stability, evidenced by higher income and employment, also significantly influences voter turnout, highlighting the critical role of financial stability in facilitating political participation. COVID-19 related variables provide mix insights into their impact on voter turnout, with satisfaction with the government's handling of the pandemic negatively impacting turnout, while views on public health measures positively impact turnout. Duration of residence in the current city also affects voter turnout, with longer residence fostering greater participation. Certain religious affiliations and regional factors also impact voter turnout. Agnostic and Jehovah's Witnesses show positive and negative impacts respectively, while regions like Nunavut, Saskatchewan, Quebec, and Northwest Territories show negative impacts. Other factors, such as education, gender, age, immigrant status, language, origin of the immigrant's country, frequency of political discussions at home, and attitudes towards immigrants, do not have a significant impact when considered alongside other variables. This study highlights the multidimensional nature of voter turnout, emphasizing the interplay of demographic, social, economic, and political factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".