Brexit may hurt U.S. and UK tech companies
Bibliographic record
Abstract
Silicon Valley's biggest businesses could face tougher regulations following Britain's decision to withdraw from the European Union, and some might have to leave London to attract the best employees. Britain's exit from the European Union could make Europe less-friendly to Silicon Valley tech companies.Many count on Europe for a quarter or more of their business, and they might find Europe a more challenging environment in which to operate.It could take a year or more to see the full impact, but the Associated Press lists 4 ways U.S. and United Kingdom-based companies could be affected.--First, Silicon Valley could lose a moderating voice.Apple, Google, and other U.S. companies have been subject to tough regulations from the E.U. in the past, but industry groups say they could face even tougher rules without Britain in the mix.Britain's acted as the moderating balance against Germany, France and other countries that prefer stricter oversight.--Next, there may be a new set of regulations.While the exit could give companies a chance to lobby UK policy makers directly, it'll also make things more complex and expensive with another set of rules to comply with.--Third, border controls could drive out U.S. companies.For legal and tax reasons, many U.S. tech companies have their European headquarters in Ireland, and several have big sales operations and teams of software developers in London.It's easier to hire people in London -- primarily immigrants -- because it's more attractive than the rest of Europe.--And lastly, UK tech companies may also leave.Banks and financial services companies are some tech firms biggest customers, and are expected to head for the European continent if Britain's exit leads to new tariffs or other barriers to financial transactions.Tech industry analysts think tech companies are likely to leave as well to be closer to their customers.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.064 | 0.030 |
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".