Bibliographic record
Abstract
Recent studies have demonstrated the quantitative importance of entry, exit, growth and decline in the industrial population. It is this turnover that rewards innovative activity and contributes to productivity growth. While the size of the entry population is impressive - especially when cumulated over time - the importance of entry is ultimately due to its impact on innovation in the economy. Experimentation is important in a dynamic, market-based economy. A key part of the experimentation comes from entrants. New entrepreneurs constantly offer consumers new products both in terms of the basic good and the level of service that accompanies it. This experimentation is associated with significant costs since many entrants fail. Young firms are most at risk of failure; data drawn from a longitudinal file of Canadian entrants in both the goods and service sectors show that over half the new firms that fail do so in the first two years of life. Life is short for the majority of entrants. Only 1 in 5 new firms survive to their tenth birthday. Since so many entrants fall by the wayside, it is of inherent interest to understand the conditions that are associated with success, the conditions that allow the potential in new entrepreneurs to come to fruition. The success of an entrant is due to its choosing the correct combination of strategies and activities. To understand how these capabilities contribute to growth, it is necessary to study how the performance of entrants relates to differences in strategies and pursued activities. This paper describes the environment and the characteristics of entrants that manage to survive and grow. In doing so, it focuses on two issues. The first is the innovativeness of entrants and the extent to which their growth depends on their innovativeness. The second is to outline how the stress on worker skills, which is partially related to training, complements innovation and contributes to growth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".