Codification, patents and the geography of knowledge transfer in the electronic musical instrument industry
Bibliographic record
Abstract
Recent research in economic geography has emphasized tacit knowledge as the basis of industrial learning. In contrast, codification and the practices of industrial writing have received little attention for the roles they play in mobilizing knowledge across space. This paper offers insight into the geographies of codification through an examination of technology transfer in the electronic musical instrument industry between 1965 and 1995. The research draws on a variety of primary and secondary data that include interviews with inventors, biographical accounts and patent analysis. These sources offer perspective on the career trajectories of three U.S. inventors who transferred knowledge from various contexts in California's high‐tech industry to the Japanese firm, Yamaha. Conceptually, the paper draws on the actor–network theory and Latour's idea of translation to highlight the detours inventors must take to register novelty. The analysis reveals the problematic nature of codified knowledge and its transfer; in this case codified knowledge was mobile internationally but not locally, at least until it reached Japan. The paper argues for the need to understand how texts such as patents are produced—the context of their authorship, the geographies of their circulation and their efficacy for shaping further innovative practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".