Geographies of Knowledge Sourcing and the Complexity of Knowledge in Multilocational Firms
Bibliographic record
Abstract
The rise of the knowledge economy has placed innovation at the center of models of competitive advantage. Access to more valuable forms of knowledge remains contested as the geography of its production is uneven and as some knowledge assets are relatively immobile. Within this fractured knowledge landscape multilocational firms have clear advantages. They can exploit numerous localized pools of knowledge, they can shape the character of knowledge development in different places, and they have some control over who can tap local knowledge assets. Surprisingly, we still have little detailed knowledge of the technologies developed by multilocational firms across the sites where they are active. We augment the literature on multiunit firms on three fronts. First, we make use of the rich, technological information in patent data to show that multilocational firms operating research and development (R&D) units across US metropolitan areas produce different kinds of technological knowledge over space. Second, we provide quantitative evidence of geographic knowledge sourcing by linking the technologies produced within the R&D units of these firms to the knowledge stocks generated within the cities where they are located. Third, we report that as the number of R&D units within multilocational firms increase, so, up to a limit, the complexity of the knowledge those firms generate also increases. We show that these complexity gains are linked to the volume of knowledge sourced from local partners and to the integration of knowledge across units of the multilocational firm.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| 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".