Bibliographic record
Abstract
This study seeks to explicate the temporal dynamics of international technology diffusion in the technological community. By combining path dependence and convergence arguments, I address who adopts what and when. I specifically investigate whether country-level technological capabilities can influence an agent’s choice of a novel or established technology and, if so, how the effect of country-level technological capabilities differs before and after technology commercialization. To test my arguments, I use research proceedings published by the international Electric Vehicle Symposium (EVS) from 1990 to 2009. Empirical findings indicate that, as path dependence suggests, agents from technologically leading countries are prone to discuss a more established technology in the pre-commercialization period. In contrast, consistent with a convergence argument, evidence also reveals that agents from technologically lagging countries are more likely to adopt a well-established and successfully launched technology during the post-commercialization period. This study enriches the literature on technology diffusion and adoption by uncovering the sequential effects of path dependence and convergence on agents’ technology adoption before and after technology commercialization. It also contributes the literature on international technology diffusion by highlighting the importance of a country’s relative technological standing for determining the country’s own technological path.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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; both teacher heads agree on what is shown here.
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".