Industrial clustering and the returns to inventive activity: Canadian biotechnology firms, 1991-2000, DRUID Working Paper n
Bibliographic record
Abstract
Abstract: The paper uses detailed data on biotechnology firms to examine the specific ways in which firms benefit from knowledge spillovers and externalities in industrial clusters. We consider how biotechnology firms ’ innovative output is affected by their own R&D activity and the R&D activity of other collocated firms ’ in the same technological application (e.g., agriculture, aquaculture, therapeutics), contrasting the effects of these R&D activities for firms that are not located in clusters or located in clusters that are not focused on the firm’s application. We find that clustered firms are more innovative with the strongest effect for firms located in clusters with strong representation in their own application. For firms located in clusters strong in their technological application we also find that 1) highly concentrated R&D activity raises the productivity of own and positive externalities from other firms ’ R&D expenditures, and 2) enhanced knowledge spillovers act as a partial substitute for own and other firms ’ more formal R&D alliance-based exchange of ideas among 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 | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".