Civil 20 Recommendations on Tackling Inequality
Bibliographic record
Abstract
Economic inequality is increasing both within and across countries. Growing inequality has negative economic, social and political consequences, it constrains economic growth, undermines social cohesion and political stability. Eradicating causes of inequality and turning structural barriers to equality into opportunities is fundamental for generating strong, sustainable, balanced and inclusive growth. Transition to this growth model will depend on G20 coherent policy actions globally and nationally. In the run up to the St. Petersburg G20 summit the Civil 20 initiated preparing a report and recommendations to the G20 focused on surmounting the risks originating from growing income inequality. A special Task Force, bringing together experts from G20 member countries has been established to draft the report. Presented and discussed within the Russian G20 Presidency Civil Society Track, the report provided an independent analysis and proposals for a dialogue between a wide range of stakeholders and the G20 governors on the G20 concerted policies and actions to improve economic equality within their countries and beyond. This set of policy recommendations on how G20 can address inequality took full account of the existing authoritative, best available, consensus, analysis and evidence of the IMF, OECD, UNDP, other international organizations and relevant scholarly, civil society and policy communities, as summarized above. It built directly upon the extensive evidence and analysis of the causes and practical policy cures for income inequality in the G20 member countries, as identified in the country reports prepared by and for members of the Task Force on Inequality (currently including Australia, Canada, China, France, India, Indonesia, Mexico, Republic of Korea, Russia, Turkey and the United States). The Civil 20 proposed that G20 leaders at their St. Petersburg summit can act together to improve income and economic equality within their countries and beyond by agreeing to act together for Strong, Sustainable, Balanced and Inclusive Growth affirming the value of equality and inclusion along with economic growth and efficiency. The publication presents Civil 20 recommendations for the G20 on measures to tackle inequality and the country reports.
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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.070 | 0.054 |
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".