Nascent entrepreneurs in Canada: An empirical study
Bibliographic record
Abstract
this project. We also thank student research assistants for conducting the telephone interviews, and especially Maripier Tremblay for her dedicated attention to all aspects of this project. 2Nascent Entrepreneurs in Canada: An Empirical Study This paper presents the results of a Canadian study of nascent entrepreneurs and the start-up process. The objective of this study is to ascertain population and individual level variables concerned with the nascent entrepreneur, the timeline and process variables associated with the start-up process, and the outcomes of the start-up process and associated variables. The study is part of the Entrepreneurial Research Consortium project being conducted in ten countries. Methodology and interview schedules are harmonized across countries. Initial results in Canada show that nascent entrepreneurs are found in 1.8 % of Canadian households. After a 12 month period a third of nascent entrepreneurs have achieved an operating business (profitable), a third are still active in the start-up process and a third are either inactive or have quit. The characteristics of nascent entrepreneurs are compared to the Canadian population. Characteristics of the early-stage operating businesses are presented. Data collection is not yet complete and a 24 month, follow-up study, will be conducted by the end of 2002.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".