Bibliographic record
Abstract
One way to proxy the outcome of the R&D process is to count the number of patents a firm has generated. However, there are several problems with the use of the num-ber of patents as an indicator of R&D-output (Griliches, 1990). However, the correla-tion between changes in R&D spending and generated patents is quite high (Pakes and Griliches, 1984). Different uses of patents as indicators of technological progress range from ‘simple ’ patent counts (Johnson et al., 1995) to the use of ‘specific ’ input-output techniques to measure the interaction between sectors in the innovative process. The purpose of this paper is to examine whether the techniques that are applied to input-output tables can also be used for the typical analysis of the specific data on patents and innovations. The data used in this study denote make and use of patents or innovations by sector. By applying the techniques developed, it is possible to pinpoint the sectors that are the most important for innovative activities and the sectors that generate the highest number of patents due to interaction with other sectors. Since these specific data are scarce only Canada is investigated empirically.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".