Subjective-objective method of maximizing extracted variance (Sommev) from governance sub-indicators: Understanding the governance of countries from an integrated and impartial perspective
Bibliographic record
Abstract
The construction of governance indicators is a multidimensional construct and faces questions regarding the definition of the weights attributed to its dimensions and sub-indicators. This study compares three existing methods of constructing composite indicators with the Subjective–objective method of maximizing extracted variance (Sommev). The Sommev method establishes weights for composite indicator components considering the subjectivity of the experts' opinion and the objectivity portrayed by the information collected for each sub-indicator. To evaluate the quality of the results generated, it is proposed to verify the average variance extracted, degree of consensus and connection with the external variable. As an example, was constructed governance indicator considering 20 countries with the largest economies in the world. The governance sub-indicators used the components of the Worldwide Governance Indicators (WGI). The GDP per capita was defined as an external variable. It uses World Bank database for the year 2021. The results indicate that countries developed have the best results for the governance indicator. These results corroborate the importance of verifying failures in governance mechanisms. Identify which dimensions have the greatest influence is important to direct public policies more efficiently to foster economic development. The quality verification indicating the robustness of the results.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".