Bibliographic record
Abstract
INTRODUCTION Measuring innovative output is difficult since innovations can differ in many different dimensions. More importantly, economic growth results from innovations of different types. Economic growth clearly results from pathbreaking innovations, such as the computer, lasers, or new chemical entities. But innovations that are not at the frontier can also substantially contribute to growth. Economic growth can result when a firm introduces new products or processes that have already been adopted in other countries but that need to be adapted to special national circumstances. It can occur when firms adopt processes from other industries. Both of these imitative types of innovations involve substantial novelty. Finally, economic growth occurs from more imitative innovations — when firms succeed in improving the products of those who pioneered a new product or process but who could not develop it quickly enough. Economic progress is also enhanced by competitive pressures that ensure that good ideas become good commercial products or processes. Sometimes these changes may simply modify the product to better satisfy consumer demands. At other times, they may involve superior production processes that lower production costs and reduce consumer prices. Innovation is, therefore, best thought of as a continuous process, whose characteristics often change over the length of the product life cycle. Gort and Klepper (1982) delineated four phases of the product life cycle. In the first, new products emerge and a small number of firms work to develop a commercial product.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".