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Record W1967514392 · doi:10.1353/ces.2015.0006

Transnationalizing Home in Winnipeg: Refugees’ Stories of the Places Between the “Here-and-There”

2015· article· en· W1967514392 on OpenAlexvenueaboutno aff
Alexander Freund

Bibliographic record

VenueCanadian ethnic studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeEthnologyMedia studiesGerontologySociologyGeographyHistoryArchaeologyMedicine

Abstract

fetched live from OpenAlex

This article asks how refugees narrate home. Based on extensive interviews (oral histories) with refugees in Winnipeg, Manitoba, who arrived from Europe after the Second World War, from Central America during the 1980s, and from Afghanistan during the 2000s, this article argues that refugees are continually engaged in the process of making home, not only in the sending and receiving countries, but also in countries along their often complex and long migration routes. Listening closely to their stories forces researchers to move beyond “methodological nationalism” and a dichotomous “here-or-there” conceptualization of home, toward a transnationalizing of home. Dans cet article je parle au sujet de comment les réfugiés racontent ce qui est, pour eux, leur maison. La recherche est basée sur les entrevues (histoire orale) avec des réfugiés à Winnipeg, Manitoba. Ceux-ci sont arrivés de l’Europe après la deuxième guerre mondiale, ainsi que de l’Amérique Centrale pendant les années 80, et d’Afghanistan pendant les années 2000. Je parle du processus de s’établir un domicile, dans lequel les réfugiés se sont continuellement engagés, pas seulement dans leur pays d’origine et d’accueil, mais aussi dans les pays tout au long de leurs voies de migration, souvent complexes et longues. L’écoute attentive de leurs histoires fait en sorte que les chercheurs se déplacent au-delà du « nationalisme méthodologique » et de l’idée des pôles opposés représentée par « ici ou là », pour aller chercher les innombrables connections entre les différents pays, ainsi qu’une transnationalisation des maisons.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.436
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0230.018
Scholarly communication0.0060.004
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.364
Teacher spread0.217 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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