Identifying Breakthroughs: Using Topic Modeling to Distinguish the Cognitive from the Economic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it