Good Governance and National Well-being
Bibliographic record
Abstract
The paper was prepared by John F. Helliwell, Haifang Huang, Shawn Grover and Shun Wang in collaboration with Mario Marcel, Martin Forst and Tatyana Teplova. This paper has three main objectives. The first is to review existing studies of the links between good governance and subjective well-being. The second is to bring together the largest available sets of nationallevel measures of the quality of governance, and to assess the extent to which they contribute to explaining the levels and changes in life evaluations in 157 countries over the years 2005-2012, using data from the Gallup World Poll already analysed in some detail in the World Happiness Report 2013. The third objective is to use subjective well-being research to suggest ways in which governance can be changed so as to improve lives in all countries, as measured by peoples’ own evaluations. The paper starts with a summary of the evidence and policy implications. There follow the four main sections of the paper, a statistical appendix containing a broad range of data and results, and an extensive annotated bibliography of empirical literature linking good governance and subjective well-being.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".