Assessing sustainable development across Moldova using household and property composition indicators
Bibliographic record
Abstract
Societies are committing themselves to sustainable development by attempting to improve environmental quality, social equity, and economic welfare. As such, there continues a plea for holistic development assessment across scales; however there remains no ideal technique for achieving sustainability on neither regional nor local scale. This paper approaches this problem by constructing a multi-metric assessment system for evaluating development patterns across the Republic of Moldova. The objectives of this study were: (1) to produce a local multi-metric index that captures the three major dimensions of sustainable development for Moldova; (2) to quantitatively evaluate the interrelatedness of sub-metrics used for creating the local composite index of sustainable development; and (3) to visualize and interpret spatial patterns of sustainable development across Moldova. A local sustainable development index (LSDI) was produced using household and property composition indicators from a 2005 demographic and health survey for the Republic of Moldova. Total sample size and aggregated spatial reference was 11,066 households and 399 geographic locations, respectively. The LSDI used a 15 submetric optimum, equal weighting, 1e5 ordinal scale standardization, and additive construction. Spearman's rank correlation coefficient analysis was used to evaluate sub-metric quantitative relationships, and local Moran's I-test to interpret geographic patterns of sustainable development. Results revealed that a wealth sub-index had greatest collinearity with other sub-metrics. Geographically, Moldova's improved sustainability levels were found in large urban areas, suggesting needed prioritization of development resources to the hinterland. For regional sustainable development assessments, this approach provides the transferability to other locally referenced datasets throughout the world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".