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Record W2735599278

Geographic and Upward Mobility of US and Canadian Citizens in Mexico: Migrant Economic Strategies and Government Responses

2013· article· en· W2735599278 on OpenAlexaboutno aff
Ève Bantman-Masum

Bibliographic record

VenueAutrepart · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Capital (architecture)Competition (biology)Geographic mobilityBusinessReal estateEconomic growthEconomicsPopulationGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

This article based on an ethnographic study explores the economic strategies of U.S. and Canadian migrants to Merida (Mexico), for whom geographical mobility also means social mobility. Real estate deals involving foreign buyers and sellers add up to everyday expenses, resulting in the transfer of millions of US dollars from North to South America. The development of this type of migration – minor but economically important – has already prompted the governments of Mexico, the United States, Canada – and even Panama – to introduce new, increasingly sophisticated regulations defining the rights and status of migrants depending on their contribution to the local economy. These regulations also allow taxing mobile citizens and determine access to public services in the countries of origin as well as in the host countries. They aim at regulating the circulation of goods and services in the region and reflect both the competition between the states resulting from these mobility patterns and the willingness of the governments of North America to cooperate to limit capital flight and tax evasion.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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 designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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