The Last Mile in Analyzing Wellbeing and Poverty: Indices of Social Development
Bibliographic record
Abstract
Development practitioners worldwide increasingly recognize the importance of informal institutions—such as norms of cooperation, non-discrimination, or the role of community oversight in the management of investment activities—in affecting well-being, poverty, and even economic growth. There has been little empirical analysis that tests these relationships at the international level. This is largely due to data limitations: few reliable, globally representative data sources exist that can provide a basis for cross-country comparison of social norms and practice, social trust, and community engagement. The International Institute of Social Studies now hosts a large database of social development indicators compiled from a wide range of sources in a first attempt to overcome such data constraints, at a low cost (http://www.IndSocDev.org). The Indices of Social Development are based on over 200 measures from 25 reputable data sources for the years 1990 to 2010.These measures are aggregated into six composite indices: civic activism, interpersonal safety and trust, inter-group cohesion, clubs and associations, gender equality, and inclusion of minorities. Not all data sources provide observations for indicators in each country, but together these data sources allow for comprehensive estimates of social behavior and norms of interaction across a broad range of societies, and increasingly with possibilities to track changes over time. This paper presents the database, highlights the differences, similarities, and complementarities with other measures of well-being, including those around income poverty, multidimensional poverty, and human development.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".