Not Invented Here? Innovation in Company Towns
Bibliographic record
Abstract
We examine variation in the concentration of inventive activity across 72 of North America's most highly innovative locations. In 12 of these areas, innovation is particularly concentrated in a single, large firm; we refer to such locations as "company towns.'' We find that inventors employed by large firms in these locations tend to draw disproportionately from their firm's own prior inventions (as measured by citations to their own prior patents) relative to what would be expected given the underlying distribution of innovative activity across all inventing firms in a particular technology field. Furthermore, we find such inventors are more likely to build upon the same prior inventions year after year. However, smaller firms in company towns do not exhibit this myopic behavior; they draw upon prior inventions as broadly as their small-firm counterparts in more diverse locations. In addition, we find that inventions by large firms in company towns have less impact than those produced elsewhere, although the difference is modest, and that the impact is disproportionately appropriated by the inventing firms themselves. Finally, the geographic scope of impact realized by company town inventions is narrower, whether produced by large or small firms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".