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Record W3215184814 · doi:10.32920/ryerson.14660913.v1

Canadian snowbirds in Mexico: transnational life experiences - a reverse perspective

2021· preprint· en· W3215184814 on OpenAlexaffabout
Adriana Espinosa de los Monteros Romo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicImmigration and Intercultural Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTransnationalismPhenomenonDiasporaPerspective (graphical)PoliticsGeographyHomogeneousIdentity (music)SociologyGender studiesPolitical scienceEconomic geography

Abstract

fetched live from OpenAlex

Much has been said of migrants coming from all over the world to Canada, but there is a rarely interest in the other side of the fence. Every year thousands of Canadians escape from the cold weather during winter season, they are the so-called snowbirds. This paper portrays the life experiences of seven Canadian snowbirds who shared the journey of spending three to seven months each year in five different cities in Mexico. Through the lens of transnationalism, this paper sheds light on a better understanding of this growing phenomenon. It explains how this seasonal migration has developed transnational behaviours in the life of the snowbirds; reflected in their mobility, identity, social networks, political awareness, as well as their cultural and economic practices. This study is not focusing on a specific community but rather on the broader phenomenon across Mexico given Canadian snowbirds are not a homogeneous diaspora in Mexico. Keywords: Canadian snowbirds, Mexico, transnationalism, seasonal migratio

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)0.001
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 score0.044
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.006
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.350
Teacher spread0.318 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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