A mega-index for the Americas and its underlying sustainable development correlations
Bibliographic record
Abstract
Indicators and their composite indices have been embraced as development tools for guiding humanity toward a sustainable destination. In response, public and private organizations have generated hundreds of these metrics, making their application overwhelming to policymakers, planners, and scientists. Past reviews have revealed that a majority of common development indices have theoretical or quantitative shortcomings, supporting that there is no consensus regarding their theoretical basis, design, use, thresholds-of-effect, or validation. In response, this study was designed around four guiding research questions: (i) What are the underlying development themes within a collection of established sustainability indices, and what distinguishes winning locations from losing ones? (ii) Are the three major divisions of sustainability (economic growth, social equity, environmental integrity) equally represented by current sustainable development measuring initiatives? (iii) Could just a few common and freely available indicators capture all present dimensions of sustainable development? (iv) Would a new sustainable development mega-index research paradigm improve humanity’s ability to assess progress toward sustainability? Those questions were investigated using data from 30 mostly contiguous Western Hemisphere nations and three amassing methodological objectives. First, 31 known indices were reduced into underlying dimensions (factors) of sustainable development. Next, those factors were combined (aggregated) into the first mega-index of sustainable development (MISD). Finally, 11 common development indicators were explored regarding collinearity and explanatory power of the sustainable development dimensions and MISD. Seven latent dimensions (sub-metrics) captured over 85% of the variation of the original 31 indices, with socioeconomic themes dwarfing environmental ones. The factors conveyed: (F1) socioeconomic well-being synergies; (F2) economic freedom and democracy; (F3) environmentally efficient happiness; (F4) ecosystem wellbeing; (F5) peace to economic vulnerability tradeoff; (F6) natural resources protection; and (F7) environmental stewardship and risk resilience. MISD is the geometric mean of the seven sub-metrics,which were directed toward sustainability, and rescaled (normalized) 0 (worst case) to 100 (best case). Geographically, this study ranked Belize best overall, followed by Guyana, Panama, Uruguay, and Canada; Barbados ranked worst, preceded by Haiti, Trinidad and Tobago, Mexico, and Cuba. Winning countries were characterized by low population density, increased forestland, decreased urban, and larger country area. Child mortality and population growth rate remained negative predictors of socioeconomic conditions; however per-capita CO2 sacrificed ecological integrity for improved human well-being. Mega-index creation will serve as an important scientific stepping-stone for improving accuracy and simplifying valuations of sustainable development, thus others should follow.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".