Bibliographic record
Abstract
The Good Country Index measures the contribution of a single country in the humanity and health aspects that are beneficial to the planet. Countries which are globally good for our planet do not necessarily have to be good for their own citizens. The Good Country Index is based on the following seven categories: science and technology, culture, international peace and security, world order, planet and climate, prosperity and equality, and health and well-being. The Good Country Index is focused on the external effects, in contrast to other global indices (for example, the Human Development Index, or the Social Progress Index) showing the level of development of a single country in benefiting its own citizens. The authors verify if these global indices may be good proxies as potential predictors, as well as indicators of a country's ‘goodness’. Non-spatial analysis included analyzing relationships between the overall Good Country Index and the seven contributing categories, as well as between the overall Good Country Index and other global indices. Data analytics was used for building various predictive models and selecting the most accurate model to predict the overall Good Country Index. The most important rules for high and low index values were identified. Spatial analysis included spatial autocorrelation to analyze similarity of index values of a country in relation to its neighbors. Hot spot analysis was used to identify and map significant clusters of countries with high and low index values. Similar countries were grouped into geographically compact clusters and mapped.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".