Age Inequalities in Political Representation: A Review Article
Bibliographic record
Abstract
Abstract People in political decision-making across the globe tend to be much older than the average voter. As such, parliaments and cabinets are unrepresentative of the larger population. This has consequences: it risks favouring policies geared towards the interests of older cohorts, it might alienate youth from voting and could push parties to appeal (even more) to older voters. In this review, we synthesize the growing literature on youth representation. We do so by: (1) delineating the group of young politicians, (2) discussing why youth ought to be present in politics, (3) empirically depicting the state of youth representation, and (4) illustrating the factors that help or harm youth to enter politics. This synthesis shows the degree to which young people are absent from decision-making bodies across the national, subnational and supra-national levels and attempts to make sense of the reasons why there is such a dearth of youth as candidates and representatives. We conclude by discussing gaps in research and suggesting several avenues for future work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".