Learning by Doing and the Locus of Innovative Capability in Biotechnology Research
Bibliographic record
Abstract
Innovative capability, the knowledge a firm uses to innovate, is an input into and an output of the process of innovation. In this paper, I put forward the notion that innovative capability, similar to experience in production, accumulates by learning by doing and that innovation is characterized by a learning curve. Using patent data from 20,886 scientists working in 611 biotechnology firms in the U.S. and Canadian biotechnology industry from 1970 to 2007, I estimate a learning curve in innovation and determine the loci of innovative capability. Although knowledge stocks in the different loci accumulate over time in day-to-day firm activities, empirical results suggest that the individual is the primary repository of innovative capability and that experience working together in teams has a secondary influence on productivity. Contrary to prior learning curve research, accumulated firm experience has no direct effect on productivity. However, when individuals possess relevant domain knowledge and have experience working together, they benefit from knowledge spillovers within the firm. This suggests that knowledge stocks in the different loci are complementary to one another and that the comingling of these disparate bins of knowledge is an important facet of innovative capability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".