Developing Methods for Assessing the Introduction of Smart Technologies into the Socio-Economic Sphere Within the Framework of Open Innovation
Bibliographic record
Abstract
The post-industrial society is transforming into a smart society, in which smart technologies control all the main processes.The study aims at proposing indicators for assessing the spread of smart technologies in various spheres of human life with due regard to the introduction of open innovations.The methodological basis of the study was an approach focused on the study of the processes of development of open innovations and the smartization of society with the involvement of special methods.Special methods include document analysis based on a literature review conducted by the authors, content analysis using multiple correspondence analysis and the cluster analysis method.This is the first study to use the Smart Progress Index like other social development indices, including the Social Progress Index, the Physical Quality Life Index and the Sustainable Economic Welfare Index.The Smart Progress Index will determine the state of society and the level of its development in technological, geopolitical, socio-economic, demographic and environmental terms.There are three indicators of the Smart Progress Index: 1) the scale of smart technologies; 2) the conditions and intensity of introducing smart technologies; 3) the results of smart technologies.Each aspect includes several components represented by seventeen indicators.
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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 | 0.038 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".