Foreign R&D satellites as a medium for the international diffusion of knowledge
Bibliographic record
Abstract
Abstract I examine the extent to which foreign R&D satellites of multinational firms act as a medium for the international diffusion of knowledge. Using patents from the United States Patent and Trademark Office, I compare the frequency with which headquarters patents are cited by third‐party firms in the satellite's host country relative to a control group of patents, and this both before and after the establishment of the satellite (using a difference‐in‐differences approach). The results suggest that the satellite increases the flow of knowledge from the multinational's headquarters to firms in the satellite's host country. This satellite effect on knowledge diffusion is largest in host countries and sectors with strong but not world‐class capabilities that have both the motivation and absorptive capacity to learn from foreign parties. The findings also suggest that knowledge diffusion is greatest when satellites are staffed with inventors that have previously either patented with other local firms (thus having stronger local social networks) or with the headquarters (thus having headquarters knowledge).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".