Canada has helped with the Canadian language data. This work has been supported by the National Science
Bibliographic record
Abstract
Convergence in per capita income turns on whether technological knowledge spillovers are global or local. Global spillovers favor convergence, while a geographically limited scope of knowledge diffusion can lead to regional clusters of countries where differences in income per capita levels persist. This paper estimates the importance of geographic distance for knowledge spillovers, how this changed over time, and whether international trade, foreign direct investment, and communication ßows serve as important channels for spillovers. The analysis examines the productivity effects of research and development expenditures in the worlds seven major industrialized countries between 1970 and 1995. I Þnd that (i) the scope of knowledge diffusion is severely limited by distance: the geographic half-life of spillovers, the distance at which half of the spillovers have disappeared, is estimated to be only 1,200 kilometers; (ii) technological knowledge has become signiÞcantly more global between the early 1970s and the 1990s; (iii) trade patterns account for the majority of all differences in bilateral spillover ßows, whereas foreign direct investment and communication ßow differences account for circa 15 % each; (iv) these three channels together account for almost the entire localization effect that would otherwise be attributed to geographic distance.
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.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.233 | 0.035 |
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".