Entrepreneurship Under Pressure: Global Entrepreneuship Monitor 2002: The Netherlands
Bibliographic record
Abstract
The Global Entrepreneurship Monitor(GEM)—designed to be a long-term, multinational project—examines therelationship between entrepreneurship and economic growth. Three key pointsunderpin the GEM research: (a) whether the level of entrepreneurial activityvaries between countries and, if so, to what extent; (b) whether the level ofentrepreneurial activity affects a country's rate of economic growth andprosperity; and (c) what makes a country entrepreneurial. The national teams ineach of the participating countries assemble three sets of data: (1)specially-designed surveys of the adult population in each GEM country; (2)in-depth interviews with experts on entrepreneurship in each country; and (3) awide selection of national economic and demographic data. This GEM report focuses on the Netherlands perspective, investigatingentrepreneurial activity in the country in 2002, in both global and Europeanperspective; the characteristics of the Dutch involved in entrepreneurialactivities; and the overall entrepreneurial climate in the Netherlands.The total entrepreneurship activity rate declined to 29%, lower than theEastern European rate (52%) or that of the ten other EU Member States (37%).Moreover, Dutch entrepreneurial activity compares unfavorably with otherEnglish-speaking countries (including New Zealand, Australia, Canada and theUSA). The Netherlands has a favorable entrepreneurial climate. Among thefindings:two in three persons entrepreneurially active are male, peakingbetween the ages 25 and 35 for men, and 35 to 45 for women.Mostentrepreneurial activities (50%) are in services. The market for informalentrepreneurship is weakly developed by international comparison. At the same time, the experts consulted underlined that negative attitudestoward failure and risk, as well as the insufficient attention toentrepreneurship in the educational system, are some of the major weaknesses ofthe Dutch entrepreneurial environment. Another problem, also signaled in the2001 report, is the limited RD in the long run, improving theattitude toward risk and failure and raising entrepreneurial awareness througheducation will be issues of concern.(CBS)
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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".