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Record W4240224541 · doi:10.32920/ryerson.14668056

There's no place like home: Hanaian and Nigerian-Canadian children sent "back home"

2021· preprint· en· W4240224541 on OpenAlexaffabout
Nicole Agyei-Odame

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsImmigrationSocializationTransnationalismIdentity (music)Gender studiesVariety (cybernetics)SociologyPolitical sciencePoliticsSocial science

Abstract

fetched live from OpenAlex

For many African immigrants to Canada, their reason of relocating can fall under a variety of push and pull factors of migration. Immigrants often settle in the host country and then have children. Many scholars have showcased the benefits of transnational ties for immigrants to their home country but rarely has this been examined through second generation immigrant children as being vessels of which this occurs. This research uncovered reasons why some Ghanaian and Nigerian-Canadian parents decided to send their Canadian born children to Ghana or Nigeria temporarily. Through qualitative data interviews with Ghanaian and Nigerian-Canadian parents from the Hamilton and the Greater Toronto Area, this study explored how transnational identity impacted this type of migration for second generation African immigrant children in Canada. Through Durkheim’s socialization theory, the findings and themes explored the various aspects of transnational relationships and identities. Key Words: Transnationalism, Bifocality, Second Generation, Ghanaian/Nigerian-Canadian, Back Home, Socialization, Identity

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 score0.062
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.009
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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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