The Pros and Cons of Ius Pecuniae: Investor Citizenship in Comparative Perspective
Bibliographic record
Abstract
The aim of this paper is to look at economic aspects of citizenship and compare states offering naturalisation to investors. By analysing different investor citizenship programs, the paper highlights the normative tension between those states that seek to maximize their economic utility and grant citizenship to investors by waiving all other naturalisation requirements, and those that uphold genuine ties with the polity as the core of citizenship by retaining them. The paper is developed as a two-level analysis of investor citizenship, starting from a global overview of facilitated access to citizenship, which is a common, yet seldom used discretionary tool of the governments. In the context of the global comparison, the paper highlights the distinction between the facilitated naturalisation for investors in countries that offer residence in the first instance (e.g., the UK, the U.S., Canada, Belgium, Australia, Singapore), and those that waive other regular naturalisation criteria (e.g., Commonwealth of Dominica and St. Christopher and Nevis). Following the global overview, the paper offers a more in-depth comparison of European countries that offer citizenship by investment while dropping other requirements, such as residence, language and knowledge of the country for these applicants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".