Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.023 |
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 it