Votes at 16? How the Rest of the World Does it
Bibliographic record
Abstract
In recent years, debates about reforms of the voting age – from 18 to 16 years of age – have been gearing up in many democracies around the world. Campaigns for a lowering of the voting age have emerged in more than 25 countries, with proposals in Ireland, Canada, and Australia gaining substantial traction. Most recently, Germany and Belgium pledged to enfranchise 16- and 17-year-olds for European elections and in New Zealand a bill to include young people in local elections is currently passing through the parliamentary stages. The voting age for Scottish elections was lowered in 2015, with Wales following suit five years later. But there has been little movement on demands for 16- and 17-year-olds to be allowed to vote in UK elections. Westminster has not always been so slow to change. Britain was the first country to lower the voting age from 21 to 18 in 1969, leading a wave of reforms of the voting age in democracies around the world. Debates about a further lowering of the voting age for UK elections have so far often focused on normative questions: what it means to be a voter and when young people achieve important milestones of adulthood. While debates on these questions are difficult to settle, much can be learnt about how voting age reforms affect young people’s political engagement and relationship with democracy from countries that have already lowered the minimum voting age.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".