Bibliographic record
Abstract
Abstract Turnout is in decline in established democracies around the world. Where, in the mid-1800s, 70–80 percent of eligible voters regularly participated in US Presidential elections, turnout has averaged just 53.7 percent since 1972. Average turnout in general elections in the UK has fallen from 76.6 percent during the period 1945–92, to 64.7 percent since 1997. Average turnout in Canadian federal elections has fallen from 74.5 percent during the period 1940–79, to 62.5 percent since 2000. For most democrats, these numbers are a cause for alarm. Compulsory voting is amongst the most effective means of raising turnout. However, compulsory voting is also controversial. Most of us think that coercion may only be employed against the citizenry if it is backed by a justification of the right kind. Opponents of compulsory voting charge that no such justification is available. This article resists this line of argument in two ways. First, I offer an argument from free-riding which, though gestured towards by others, and widely criticized, has yet to be defended in any depth. Second, I consider a range of objections to compulsory voting as such, arguing that none succeeds.
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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".