A New Method for Identifying Recombinations of Existing Knowledge Associated with High‐Impact Innovation
Bibliographic record
Abstract
How existing technologies and ideas are recombined into new innovations remains an important question, particularly as the store of prior technology, art, and work expands at an increasing rate. Yet, methodologies for identifying effective recombinations remain a nascent area of research. This paper extends our previous work, which developed a network methodology for assessing a scientific article's recombinations of prior work. The methodology uses information from the entire co‐citation network of all papers recorded in the W eb of S cience to identify combinations of prior work that are conventional or atypical and then identifies the virtuous mix of conventional and atypical pairings associated with high impact work. Here, we summarize our prior method and findings, present new findings, and perform a case study application to the field of management science. First, the results show that despite an ever‐increasing frontier of possible new combinations of prior work, atypical combinations of prior work are becoming increasingly rare with time, while the distribution of conventional pairings is increasing with time. Second, our analyses show that with time the atypical pairings found in hit papers have a relatively stable mean rate at which they become conventional pairing. Nevertheless, the variance around the mean is growing significantly, which indicates that there is a greater tendency over time for novel pairings either to be virtually never used again or to become conventional pairings.
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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 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".