The Innovation-Based Human Development Index Using PROMETHEE II: The Context of G8 Countries
Bibliographic record
Abstract
The current Human Development Index (HDI) has a promising potential to consider further dimensions, the technological dimensions in specific, in order to absorb various innovational aspects whenever human development is to be benchmarked among countries. Hence, the innovation-based HDI was developed herein using one of the well-known Multi-Criteria Decision Making (MCDM) techniques: the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) considering a mixture of technological criteria, including the Global Innovation Index (GII) itself. The G8 countries, as leading countries worldwide, were investigated in this regard in order to attain such a benchmarking attempt. The model was formulated using seven criteria selected from the World Bank (WB) Open Data (such as High-technology exports as a percentage of manufactured exports, Research and development (R&D) expenditure as a percentage of GDP, and Trademark applications, …, etc.) along with the GII, for the purpose of conducting an MCDM-based evaluation model for the G8 countries. The results of the developed index affirm that the ranking of the G8 countries has distinctly been changed as a consequence of considering technological and innovational aspects, compared to the original HDI (i.e., USA +4—from 5th to 1st; Canada −4, from 2nd to 6th). By utilizing MCDM methods (including PROMETHEE II), this paper also affirms that an infinite number of indexes can be developed in the future by employing a huge number of WB indicators with respect to various MCDM approaches. Hence, international communities are in need of setting up commonly accepted guidelines in order to facilitate having a unified prioritization (i.e., unified preference) regarding the potential criteria and/or indicators to be considered globally for better sustainable development.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".