Indicators for complex innovation systems
Bibliographic record
Abstract
Performance indicators such as national wealth (GDP per capita), R&D intensity (GERD/GDP) and scientific impact (citations/paper) are used to compare innovation systems. These indicators are derived from the ratio of primary measures such as population, GDP, GERD and papers. Frequently they are used to rank members of an innovation system and to inform decision makers. This is illustrated by the European Research Area S&T indicators scoreboard used to compare the performance of member states. A formal study of complex systems has evolved over the past few decades from common observations made by researchers from many fields. Complex systems are dynamic and many of their properties emerge from the interactions among the entities in them. They also have a propensity to exhibit power law or scaling correlations between primary measures used to characterize them. Katz [Katz, J.S., 2000. Scale independent indicators and research assessment. Science and Public Policy 27, 23–36] showed that scientific impact (citations/paper) scales with the size of the group (papers). In this paper it will be shown that two other common measures, R&D intensity and national wealth, scale with the sizes of European countries and Canadian provinces. Some of these scaling correlations are predictable. These findings illustrate that a performance indicator derived from the ratio of two measures may not be properly normalized for size. This paper argues that innovation systems are complex systems. Hence scaling correlations are expected to exist between the primary measures used to characterize them. These scaling correlations can be used to construct scale-independent (scale-adjusted) indicators and models that are truly normalized for size. Scale-independent indicators can more accurately inform decision makers
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".