Keystone Technologies: Identifying Technologies of Greatest Consequence to World Technical Progress by Modeling Knowledge Dynamics on the Global Innovation Network
Bibliographic record
Abstract
In this paper we model technological knowledge as a dynamic network. After establishing the structure of technological knowledge we simulate its dynamics and evolution over time. The modeling exercise reveals technological trends, allows us to rank technologies in terms of their contribution to overall technological progress, and makes it possible to identify technologies that are the drivers of innovation at the global level, which we term “keystone technologies.” The sphere of technologically relevant knowledge is conceptualized as a reflexive, directed, link- and node-weighted complex network, with distinct spheres of knowledge (or technology domains) representing network nodes and learning (or knowledge flows) across domains acting as inter-nodal links. The empirical knowledge network is constructed from a sweeping patent database, including records from more than 100 patent-granting authorities over the 22-year period spanning 1991-2012. We instantiate the nodes of the knowledge network from patent categories of the International Patent Classification (IPC) system. Links between technology domains, representing knowledge transfer between fields of technology, are constructed from patent citations. The analysis might be beneficial to researchers modeling knowledge networks, historians of science and technology, technology and R&D managers tasked with monitoring technology trends and identifying new projects, and public officials working on innovation policy or making allocative decisions regarding scientific and technological research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".