A Decision Support System using ANFIS to allocate resources. An economic and patent-based analysis on European regions
Bibliographic record
Abstract
This thesis is the result of a research project carried out from October 2016 to February 2017 at École Polytechnique de Montréal, Canada. The developed research mainly focused on the building of a Decision Support System in order to provide policy makers useful indication for a better allocation of resources, with a view to the Smart Specialisation Strategies (SSS), which aimed to consolidate the regional strengths and make effective and efficient use of public investment in R&D. After a literature review basing on forecasting techniques for innovative purposes, we chose ANFIS to understand how - and to what extent – the competitiveness drivers promote technological development and how the latter contributes to the economic growth of European regions. We used both economic, spatial and patent-based data to train, test and validate the models. What emerges is that an increasing of investments on R&D, both private and public, enhances the employment rate and the number of patents per capita, which related to several combinations of specialization and diversification indicators, lead to an increasing of GDP per capita of the regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".