Greentech homophily and path dependence in a large patent citation network:Policies and practices from canada, new zealand and the european union
Bibliographic record
Abstract
We propose a method to identify the main technological trends in a very large (i.e., universal) patent citation network comprising all patented technologies. Our method builds on existing literature that implements a similar procedure, but for much smaller networks, each covering a truncated sub-network comprising only the patents of a selected technology field. The increase of the scale of the network that we analyse allows us to analyse so-called macro fields of technology (distinct technology fields related by a coherent overall goal), such as environmentally friendly technologies (Greentech). Our method extracts a so-called network of main paths (NMP). We analyse the NMP in terms of the distribution of Greentech in this network. For this purpose, we construct a number of theoretical benchmark models of trajectory formation. In these models, the ideas of homophily (Green patents citing Green patents) and path dependency (the impact of upstream Green patents in the network) play a large role. We show that a model taking into account both homophily and path dependence predicts well the number of Green patents on technological trajectories, and the number of clusters of Green patents on technological trajectories.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".