Cooperation, scale-invariance and complex innovation systems: a generalization
Bibliographic record
Abstract
The focus of this paper is the question “Can scale-invariant properties of collaborative research activities of a complex innovation system be quantified, modeled and used to inform decision makers about the effect that cooperation has on the impact of published peer-reviewed research?” Over the past few decades cooperative research activities have been extensively studied. Presently, encouragement and support for collaborative research and training is a cornerstone of many innovation policies and programs. Concurrently, the study of complex systems has produced tools and techniques that can be applied to the study of innovation systems. They have been shown to be complex systems with scale-invariant properties that can be measured and modeled providing novel insights to decision makers. An important factor contributing to the emergence of scale-invariant properties is the inseparable tension between competitive and cooperative activities among actors within a complex system. Peer-reviewed papers index in the 1990–2010 Web of Science and citations to these papers are used as a partial measure of size and impact, respectively. Documents are classified into 14 natural, health and applied sciences fields. Numbers of authors and country information from each paper are used to classify documents into various types of cooperation. Scale-invariant correlations between impact and sizes where prepared to provide measures and models used to explore the effects of cooperation types. It is shown that collaborative research tends to have greater impact and for a longer period of time that non-collaborative research. Cooperation in the more applied fields show higher growth of impact when compared to the growth of their sizes than cooperation in fields closer to the basic or ‘blue sky’ end of the R&D spectrum. Cooperation in a complex innovation system can have significant effects on the relative growth of impact with respect to growth of size and it enhances the sustainability of the Matthew Effect over time. Cooperative activities appear to sustain self-organization in a complex innovation system.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.023 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".