International Innovation Comparisons: Insight or Illusion?
Bibliographic record
Abstract
Recent developments in statistical methodology have facilitated international innovation comparisons. These, however, inevitably reflect both the industrial structure of the underlying economies, and the innovativeness of firms within each element of the industrial structure. The authors consider the extent to which structural differences between economies can influence international innovation comparisons. The impact of structural differences is considered both in conceptual and in empirical terms, with the aid of data from the first and second Community Innovation Surveys (CIS 1 and CIS 2). Conceptual analysis suggests a very restricted range of scenarios under which structural adjustment will be effective. Empirical results are more reassuring, however, suggesting that, although structural effects are in some cases quite large, they do not significantly distort international innovation relativities. The comparisons do, however, highlight significant inconsistencies between different innovation indicators and suggest policy priorities.
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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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".