Geographies of Knowledge Sourcing and the Complexity of Knowledge in Multilocational Firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it