Bibliographic record
Abstract
INTRODUCTION As productivity growth in Western countries slowed in the post-1973 period, economists and statisticians increasingly turned to understanding the growth process. This interest has led to studies of innovation. Unfortunately, data on innovation have been difficult to assemble. Data on patents have supported a set of studies. But many innovations are not patented and, therefore, patent data was seen as providing only partial coverage of the innovation process. Data on research and development also existed and could be used to examine differences in the tendencies of small and large firms to innovate; but R&D is only one of the inputs into innovation, and exclusive reliance on R&D data can, therefore, be misleading. Finally, case studies of particular innovations can shed light on the evolutionary process that takes place across the product life cycle. But it is difficult to know how to draw generalizations from case studies that may not be very representative of all firms. Innovation surveys have evolved in an attempt to provide more detailed data on the process that is behind economic growth. Innovation surveys extend data collection beyond R&D inputs to an examination of some of the other essential ingredients — such as the importance of technology transfer. But their chief claim to originality is the measurement of innovative output. DEFINING INNOVATION Measuring innovative output is difficult. Innovations can be described in many different dimensions.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.050 |
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".