Innovating in less developed regions: What drives patenting in the lagging regions of Europe and North America
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
Abstract Not all economically disadvantaged—“less developed” or “lagging”—regions are the same. They are, however, often bundled together for the purposes of innovation policy design and implementation. This paper attempts to determine whether such bundling is warranted by conducting a regional level investigation for Canada, the United States, on the one hand, and Europe, on the other, to (a) identify the structural and socioeconomic factors that drive patenting in the less developed regions of North America and Europe, respectively; and (b) explore how these factors differ between the two contexts. The empirical analysis, estimated using a mixed‐model approach, reveals that, while there are similarities between the drivers of innovation in North America's and Europe's lagging regions, a number of important differences between the two continents prevail. The analysis also indicates that the territorial processes of innovation in North America's and Europe's less developed regions are more similar to those of their more developed counterparts than to one another.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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