From Classroom to Polling Station: A Cross-Canada Analysis of High School Civics Curricula
Bibliographic record
Abstract
Public engagement, specifically in the form of voter turnout, has been a topic of discussion in political science since its inception. With recent declines in voter turnout, especially amongst young voters, some experts have begun to fear a generational shift towards political apathy; a shift that could lead to a heavily apolitical society as the generations who value politics are replaced by those who do not. With this context in mind, scholars have been trying to understand what drives turnout, why participation matters, and predict the attitude and behaviours of the Canadian electorate. Through these queries, it has been found that there is a direct link between political knowledge and likelihood to vote. Those with higher levels of political knowledge and understanding have been known to vote more frequently, and those who do not vote have reported one of their main reasons for not voting as a lack of information or understanding. This grounding knowledge has led many to question and study the role that education plays in a citizen’s decision to vote. The following paper seeks to understand the role that Canadian high school civics curricula have played in shaping the likelihood to vote for the youngest voting bracket in the nation. With a research question that broadly asks, what impact does a Canadian civics curriculum have on youth voter turnout?, the following research paper will employ a qualitative document analysis to better understand the possible links between education and voting.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".