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Record W4416977727 · doi:10.1177/00104140251400342

The Archipelago Capitalism of Citizenship-By-Investment

2025· article· en· W4416977727 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComparative Political Studies · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of Toronto
FundersMax-Planck-Institut zur Erforschung Multireligiöser und Multiethnischer GesellschaftenEuropean University Institute
KeywordsCitizenshipCapitalismLeverage (statistics)ArchipelagoPoliticsPhenomenonPaymentState (computer science)

Abstract

fetched live from OpenAlex

Citizenship-by-investment (CBI) programs, granting citizenship in return for financial payment or investment, have become a global phenomenon in recent years. The workings of these exceptional programs have caused controversy in real-life politics, ranging from protests, to the downfall of politicians, and to punitive bilateral and international measures. Even so, knowledge on why countries would put their citizenship up for sale has remained limited. This study combines insights from political science and legal theory to develop an original approach to understand states’ propensity to adopt investor citizenship policies as part of the offshore world, or the legal spaces of ‘archipelago capitalism’. We leverage a novel global longitudinal CBI dataset (1960–2023) to probe the empirical plausibility of this argument. In line with our expectations, we find that microstates, middle-income countries, and tax havens are more likely to implement CBI programs. CBI supply reflects a contemporary form of small state ingenuity.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.330
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it