Demonstrating a correlation between infrastructure and national development
Bibliographic record
Abstract
This paper demonstrates a correlation between the extensiveness of infrastructure and national development. This was achieved by considering kilometres of paved roads, kilometres of rail, kilometres of paved runways, quality of shipping ports and quality of urban infrastructure. Data were collected from a variety of sources including the World Bank and the United Nations databases. Measures of the quantity or extensiveness of the infrastructures were normalized based on the populations of the various countries, transforming them into per capita measures, which were then logarithmically transformed to produce indices of the extensiveness of the infrastructures. These indices were then plotted against the national development indicator, the human development index (HDI). Of the infrastructures considered, paved roads per capita index showed the strongest correlation with HDI, while quality of port infrastructure index showed the weakest correlation. When the indices for the different infrastructures were combined into a single index the correlation with HDI appeared to be strongest, highlighting the synergistic effect of different infrastructures when working in tandem. Based on this, the findings of this study support the position of holistically managing a country's infrastructure assets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".