Sustainable Competitiveness: Application of Data Modeling to Identify Predictive Factors by Country
Bibliographic record
Abstract
Competitiveness as a concept has had a significant impact on the development not only of individual companies, but also of the national and international economy. This article contributes to the analysis of competitiveness framed in the context of sustainability, responding to the call of the United Nations in relation to the Sustainable Development Goals (SDG), and more specifically in the framework of SDG 11, which mentions the importance of working in pursuit of “sustainable cities and communities”. The mixed-cut methodology starts from a conceptual inquiry into the competitiveness of states according to the World Economic Forum, highlighting its main components and a detailed examination of the associated indices to collect data that were later analyzed for the construction of models for understanding of the dynamics of the sustainable competitiveness index, thus contributing to the knowledge of the main competitive advantages and disadvantages of the states in this matter. As a result, a factor analysis of 1196 country records is obtained, which, after being contrasted with structural equation models, allowed the identification of five factors with their respective regression values, thereby identifying classification scenarios by countries with their respective predictor variables. Finally is concluded that there are different variables such as intellectual capital, social capital, natural capital and governance that are needed for a better sustainable society. The methodology approach for this paper presents a novel KDD approach in order to have a more suitable and proper 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".