Bibliographic record
Abstract
About this documentThe Global State of Democracy Indices Codebook, Version 3 is the third in a series of documents prepared by International IDEA to present the Global State of Democracy (GSoD) Indices.The Codebook presents information about all the variables included in the dataset: identifying variables, attributes of democracy, subattributes, subcomponents and indicators.The other two documents are The Global State of Democracy Indices Methodology: Conceptualization and Measurement Framework, Version 3 (Skaaning 2019), which presents the theoretical framework that guided the construction of the Indices, and The Global State of Democracy Indices: Technical Procedures Guide, Version 3 (Tufis 2019), which outlines the technical aspects of constructing the Indices.Version 3 of the GSoD Indices depicts democratic trends at the country, regional and global levels across a broad range of different attributes of democracy in the period 1975-2018 but does not provide a single index of democracy.The Indices produce data for 158 countries.The data underlying the Indices is based on a total of 97 indicators developed by various scholars and organizations using different types of source, including expert surveys, standards-based coding by research groups and analysts, observational data and composite measures.The Global State of Democracy is a biennial report that aims to provide policymakers with an evidence-based analysis of the state of global democracy, supported by the GSoD Indices, in order to inform policy interventions and identify problem-solving approaches to trends affecting the quality of democracy around the world.The first edition of the report (International IDEA 2017), explored the conditions under which democracy can be resilient and how to strengthen its capacity as a system to overcome challenges and threats.
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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.130 | 0.088 |
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".