Bibliographic record
Abstract
INTRODUCTION Innovation requires a conscious effort to develop new ideas for both products and processes. During the nineteenth century, scientific progress was less structured and typically arose from ideas developed in many different areas of the firm. While sources for ideas still flow from different areas, the twentieth century has seen increasing emphasis placed on formal research and development (R&D) facilities as a source of innovation. The renowned research laboratories of AT&T, General Electric, Dupont, and IBM epitomize the organized pursuit of scientific knowledge and its application to production problems. It is true that science played an important role in the early industrial revolution in the United Kingdom; nevertheless, in the twentieth century, the modern corporate enterprise has harnessed science and technology in a new and more extensive fashion within its organizational bounds. Systematic management rules are used to pursue scientific knowledge in the interest of economic well-being. Since research and development is seen to have a special and key role in the innovation process, this chapter investigates its importance by examining the extent to which Canadian manufacturing firms incorporate R&D into the innovation process. DEFINITIONS: EXPENDITURES ON RESEARCH AND DEVELOPMENT Because of the importance that is today attached to research and development, considerable effort has been devoted to measuring the inputs to this process. New data on the importance of research and development expenditures within different systems of national innovation have emerged.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".