GETTING TO X: AN EXPLORATION OF THE GENERATION GAP IN VOTER TURNOUT IN CANADIAN FEDERAL ELECTIONS, 1984 TO 2004
Bibliographic record
Abstract
The purpose of this project is to explore the generational component of declining voter turnout in Canada. More recent cohorts vote at much lower rates than previous cohorts despite higher levels of formal education. This project tests three possible explanations for this trend. First, it is found that different rates of political mobilization among cohorts do not play a role in declining turnout. Second, it is also found that different levels of campaign interest explain part of the cohort effect. And finally, drawing on the Relative Educational model (REM) developed by Nie, Junn and Stehlik-Barry, it is found that in the Canadian context education affects turnout in relative, as opposed to absolute terms. This call into question whether the higher levels of formal education of more recent cohorts should not be expected to increase their levels of voter turnout.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".