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Record W2074097207 · doi:10.14506/ca29.3.05

Working Mis/Understandings: The Tangled Relationship between Kinship, Franco-Malagasy Binational Marriages, and the French State

2014· article· en· W2074097207 on OpenAlexfundno aff
Jennifer Cole

Bibliographic record

VenueCultural Anthropology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersUniversity of TorontoWenner-Gren FoundationNational Science Foundation
KeywordsKinshipCommodificationSociocultural evolutionSociologyXenophobiaContext (archaeology)State (computer science)NegotiationGender studiesPoliticsColonialismFamily reunificationAnthropologyPolitical scienceImmigrationGeographySocial scienceRacismLaw

Abstract

fetched live from OpenAlex

Marriage migration and family reunification have become one of the few ways for migrants from former French colonies to gain legal entry to France. As a result, love, marriage, and kinship have become central to the politics of contemporary border control. Based on extensive research with Franco-Malagasy families in southwestern France, this article examines how couples negotiate the complexities of their binational relationships in the context of state-fostered xenophobia and suspicion. I suggest the analytic of a working mis/understanding to capture how these marriages operate. While at one level the working mis/understanding enables Malagasy women and French men to bridge their different notions of kinship, at another level it naturalizes a long-standing colonial relationship between France and Madagascar. I further consider how the sociocultural dynamics of the working mis/understanding illuminate how state regulations produce the commodification of intimate relations allegedly intrinsic to these marriages.

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)0.003
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.031
Scholarly communication0.0070.005
Open science0.0000.004
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 head0.051
GPT teacher head0.326
Teacher spread0.275 · 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

Citations51
Published2014
Admission routes1
Has abstractyes

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