Bibliographic record
Abstract
The trend of declining voter turnout across the western world has led some in Canada to call for mandatory voting. Australia is often cited as a successful example of compulsory voting in a Westminster system. While the aim to increase voter turnout is noble, there are many non-coercive methods of improving democracy and voter turnout that Canada ought to adapt before resorting to mandatory voting. Assessed methods include electoral reform, lowering the voting age, and instituting online voting; all are non-coercive ways to improve public satisfaction with the political process in Canada. Additionally, mandatory voting reduces Canadians’ ability to abstain from participating in the political system should they choose to do so which could have important philosophical implications. Furthermore,voter turnout data for Australia does not take into account important differences between registered voter turnout and voting age population turnout. Importantly, when analyzed these numbers indicate that compulsory voting in Australia is not as successful as many believe. Despite its ostensible attraction as a clear way to increase voter turnout, a legal requirement to vote is not a panacea to the issues of political distrust, dissatisfaction, and disengagement in Canada that are the root causes of low voter turnout.
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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".