Introducing the Worldwide Age Representation in Parliaments (WARP) data set
Bibliographic record
Abstract
Abstract The absence of young adults in elected legislative assemblies is a democratic deficit with potentially severe repercussions. Yet, research is rarely able to address the issue of age group representation systematically because we are lacking empirical information on the age distribution in parliaments. The Worldwide Age Representation in Parliaments (WARP) data set remedies this dearth of data. It provides information about the numerical presence of age groups in parliaments, spanning across the globe and over time and includes age data on legislators, such as the share of members of parliament (MPs) aged 30 years or under, 35 years or under, or 40 years or under. The data set also reports measures that compare the presence of legislators aged 30 years or under, aged 35 years or under, aged 40 years or under, aged 41 to 60 years, as well as aged 61 years or over in relation to the same age group in the general population of a given country. Moreover, it includes gendered figures, such as the presence of young female MPs. The WARP data set contains data for more than 700 elections in 149 countries, so far, and is freely available online. It allows for a novel analysis of the age composition of legislatures.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".