Rapid Technological Change and U.S. Entrepreneurial Risk in International Markets: Focus on Data Security, Information Privacy, Bribery and Corruption
Bibliographic record
Abstract
Research from the Kauffman Foundation shows that without startups, there would be no net job growth in the U.S. economy. Since the mid-1990s, businesses with fewer than 500 employees have created 60 to 80 percent of U.S. net new employment. Rapid technological change and the phenomenal growth of the Internet and social media during recent years results in a rational perception by entrepreneurs that rapid access is available to international markets and sales. However, lurking in the allure of international expansion is the very real threat of exposure to global data security and privacy laws, and widespread bribery and corruption. Every entrepreneur needs to consider their response before being confronted with these problems. Substantial fines, penalties, legal and other expenses and even jail time may result from running afoul of the GDPR, other data security or privacy laws, the FCPA, U.K. Bribery Act or other local anti-bribery or corruption laws. While the cost of anti-bribery compliance for entrepreneurs of early-stage enterprises is heavy, the ethical, economic, and social impact of bribery is equally onerous. A review of bribery and corruption among the United States’ top three trading partners (Canada, China, and Mexico) illustrate these risks. I discuss the Foreign Corrupt Practices Act; OECD Convention on Combating Bribery of Foreign Public Officials in International Business Transactions; the U.K. Bribery Act 2010; and offer a few thoughts about the cost of anti-bribery compliance, as well as the ethical and societal cost of corruption. I believe that this article contributes to the existing small-business literature by better equipping entrepreneurs and early-stage enterprises to recognize and understand risk as they plan to enter international markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".