International Digital Nomads: Immigration Law Options in the United States and Abroad
Bibliographic record
Abstract
Remote work has become common, allowing many people to choose to work anywhere with an adequate internet connection. Some are adopting a “digital nomad” lifestyle, moving with the seasons or years from place to place, including foreign locations. Yet, such international movement raises immigration and other legal issues. Many countries have adopted specific digital nomad visas and other immigration policies to encourage and regulate this trend. The United States is not one of them. Arguing that the United States should consciously plan for digital nomads, this article compares the current U.S. approach with the innovations of other countries, identifying the advantages and disadvantages of different options. It proposes that the United States adopt Canada’s visitor visa policy allowing remote work for foreign employers as a realistic first step in planning for international digital nomads.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".