The effect of income inequality and other socioeconomic factors on political participation in Canadian federal elections
Bibliographic record
Abstract
Voter turnout rates for Canadian federal elections have been in decline for over 50 years, and currently Canada is ranked 18 th among OECD countries in this regard.To what extent do certain socioeconomic factors have in encouraging or discouraging voters' participation in elections in Canada?This study examines previous literature on theories related to the ties between political participation and socioeconomic inequality; including the law of dispersion, relative power theory, conflict theory, and resource theory.Compiling data from external sources and creating a pseudo panel specific to this study, these theories are then tested to examine how income inequality (measured through Gini index, P90/P50 and P50/P10 ratios, and median income), age, marital status, and employment rates has effected voter turnout in Canada between 1979 and 2015.The analysis shows that the effects of both income inequality and the employment rate on turnout exhibit non-linear quadratic characteristics.Further to that, age and marital status are also shown to have positive effects on voter turnout.Employment rates specific to education are also examined but deemed generally inconclusive, however further insight and stronger data could yield better results and be cause for future study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".