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Record W3008221135 · doi:10.1080/17450101.2020.1722557

Mobilizing mobilities: birthright tourists as willful strangers in Canada

2020· article· en· W3008221135 on OpenAlexaffabout
Kristin Lozanski

Bibliographic record

VenueMobilities · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsWestern UniversityKing's University College
Fundersnot available
KeywordsCitizenshipTourismMobilitiesState (computer science)HostilityPosition (finance)Capital (architecture)ImmigrationSovereigntySociologyPolitical scienceBusinessGeographyLawSocial psychologyPsychologySocial science

Abstract

fetched live from OpenAlex

A so-called birth tourist travels to a country with birthright citizenship to give birth so that her child will be a citizen of that country. In Canada, hostility towards birth tourism has simmered since 2012. Situating this hostility within a history of Sinophobia, I analyze birth tourism websites, arguing that those who can access Canadian citizenship via birth tourism already possess network capital, a position that is not enabled but enhanced by their child’s citizenship. I argue that public concern about birth-tourism in Canada turns on the willfulness of birth tourists as strangers who impose themselves upon the state. Birth tourists combine their reproductive capacity and their capacity for mobility to subvert the sovereignty of the Canadian state: their reproduction is inherently nationalized and produces citizens who have not been vetted by the Canadian state. In this way, birth tourists invoke mobility to access citizenship without commitments and without state sanction, creating strangers within the state.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.006
Scholarly communication0.0070.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.030
GPT teacher head0.254
Teacher spread0.224 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations14
Published2020
Admission routes2
Has abstractyes

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