Trajectory integration and the impact of inventions
Bibliographic record
Abstract
• Examines trajectory integration: recombining multiple inventions within a shared pre-existing trajectory. • Inner-domain trajectory integration is associated with higher impact within the focal domain but lower impact outside it. • Outer-domain trajectory integration is associated with higher impact outside the focal domain. • Emphasizes relevance of shifting from a static to a dynamic trajectory-based perspective in domain search strategies. This study introduces the concept of trajectory integration in recombinant search, where inventors recombine multiple inventions from a shared pre-existing trajectory. As inventions within a trajectory build upon their predecessors, we theorize that inventors can learn from these interconnections to develop more effective approaches to new problems. We argue that the benefits of trajectory integration depend on whether inventions in the trajectory belong to the focal domain (inner-domain trajectory integration) or lie outside it (outer-domain trajectory integration). Analyzing 19,266 nuclear energy patent families, we find that inner-domain trajectory integration is linked to higher impact within the focal domain but lower impact outside it. Conversely, outer-domain trajectory integration is associated with impact beyond the focal domain but shows no link to impact within it. We contribute to recombinant search literature by highlighting the relevance of considering the historical context of inventions and their trajectories to better understand their value.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".