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Record W2890589340 · doi:10.1007/978-3-319-92719-0_2

Dangerous Liaisons: Money and Citizenship

2018· book-chapter· en· W2890589340 on OpenAlexaff
Ayelet Shachar

Bibliographic record

VenueIMISCOE research series · 2018
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCitizenshipExpansionismBarterPoliticsPolitical scienceGridlockPolitical economyDevelopment economicsEconomyEconomicsLawMarket economy

Abstract

fetched live from OpenAlex

Abstract Vogue predictions that citizenship is diminishing in relevance or perhaps even vanishing outright, popular among jetsetters who already possess full membership status in affluent democracies, have failed to reach many applicants still knocking on the doors of well-off polities. One can excuse the world’s destitute, those who are willing to risk their lives in search of the promised lands of migration in Europe or America, for not yet having heard the prophecies about citizenship’s decline. But the same is not true for the well-heeled who are increasingly active in the market for citizenship: the ultra-rich from the rest of the world. They are willing to dish out hundreds of thousands of dollars to gain a freshly-minted passport in their new ‘home country.’ That this demand exists is not fully surprising given that this is a world of regulated mobility and unequal opportunity, and a world where not all passports are treated equally at border crossings. Rapid processes of market expansionism have now reached what for many is the most sacrosanct non-market good: membership in a political community. More puzzling is the willingness of governments – our public trustees and legal guardians of citizenship – to engage in processes that come very close to, and in some cases cannot be described as anything but, the sale and barter of membership goods in exchange for a hefty bank wire transfer or large stack of cash.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.127
GPT teacher head0.305
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations47
Published2018
Admission routes1
Has abstractyes

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