Identifying Breakthroughs: Using Topic Modeling to Distinguish the Cognitive from the Economic
Bibliographic record
Abstract
Previous research on breakthrough innovations has used patent data to identify them and assess their impact. The main proxy for breakthroughs uses forward citation counts, where patents at the top of the distribution are considered breakthroughs. Scholars have found this metric correlates with the economic value of patents (i.e., stock market valuations), yet, it does not tell us much about their technological content. We propose a new methodology – topic modeling of patent texts – to distinguish cognitive from economic breakthroughs. In our test case analysis of 2,826 nanotechnology patents, we find that cognitive breakthroughs are more likely to be highly cited, yet the mechanisms that produce cognitive and economic breakthroughs are quite different. Moreover, patents that are cognitive as well as economic breakthroughs have a bigger and more enduring impact on future inventions. This approach gives us traction in understanding the emergence and evolution of technologies over time.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".