Bibliographic record
Abstract
In today’s age of restrictionism, a growing number of countries are closing their gates of admission to most categories of would-be immigrants with one important exception. Governments increasingly seek to lure and attract “high value” migrants, especially those with access to large sums of capital. These individuals are offered golden visa programs that lead to fast-tracked naturalization in exchange for a hefty investment, in some cases without inhabiting or even setting foot in the passport-issuing country to which they now officially belong. In the U.S. context, the contrast between the “Dreamers” and “Parachuters” helps to draw out this distinction between civic ties and credit lines as competing bases for membership acquisition. Drawing attention to these seldom-discussed citizenship-for-sale practices, this essay highlights their global surge and critically evaluates the legal, normative, and distributional quandaries they raise. I further argue that purchased membership goods cannot replicate or substitute the meaningful links to a political community that make citizenship valuable and worth upholding in the first place.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".