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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".