Innovation and Research and Development in Small and Large Firms
Bibliographic record
Abstract
INTRODUCTION Considerable economic research has been devoted to establishing whether small and large firms differ with regard to the rate of innovation or their R&D activity. On the one hand, this research was seen to have implications for aggressive American antitrust policies that focused on large firms that performed what was perceived to be a disproportionate amount of scientific research (Scherer, 1992). But more recently, the literature has focused more on the need to develop special support for R&D in small firms (Rothwell and Zegveld, 1982; Acs and Audretsch, 1990). Since the share of total employment in Canada accounted for by small firms has been increasing (Baldwin and Picot, 1995), attention in Canada has been focused on the need for policies to facilitate more innovation in this sector. The growth of the importance of small firms has led to a reexamination of the adequacy of science and technology policies, in general, and research and development R&D subsidies, in particular, that are available to this group. If an informed decision is to be made on whether aid for small firms' R&D efforts requires policies that are distinct from those designed for large firms, it is essential to assess the differences in the R&D capacity and innovation capabilities of small and large firms. For this reason, this chapter examines whether variations exist in the R&D profile and in the tendency of small and large firms to innovate.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".