Artificial intelligence beetween Big Science and Small Science: an Italian strategy to compete
Bibliographic record
Abstract
This article presents a strategic analysis on the situation and the possible developments of Artificial intelligence (AI) from a scientific but also an economic and business point of view. It contrasts the two complementary paths of Big Science and Small Science by analyzing two case studies: machine translation and the Economic Fitness and Complexity framework, a new methodology that provides superior insight into economic development with fewer but carefully chosen data. It then examine the implications of AI for the labor market and assess Italy’s position, which can become a global leader in Small Science for AI by leveraging interdisciplinarity and complexity science, ingenuity, and talent attraction.
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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 | 0.036 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.027 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.002 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".