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Record W4312061294 · doi:10.18357/bigr41202220554

Seeking Better Life Chances by Crossing Borders: The Existential Paradox and Strategic Use of Italian Citizenship by Migrant Women

2022· article· en· W4312061294 on OpenAlexvenueno aff
Rosa Gatti

Bibliographic record

VenueBorders in Globalization Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersMinistero dell’Istruzione, dell’Università e della Ricerca
KeywordsCitizenshipNexus (standard)Agency (philosophy)ExistentialismEthnographyGender studiesSociologyPerspective (graphical)Destiny (ISS module)Migration studiesInclusion (mineral)Political scienceSocial scienceLawAnthropology

Abstract

fetched live from OpenAlex

Based on long-term ethnographic fieldwork carried out in Naples (Italy) in the period 2014 to 2020, this article focuses on the rearticulation of the migration–citizenship nexus through a gender perspective. The article questions how migrant women exercise their agency despite the structural constraints that prevent their full inclusion and how they are able to cross and transgress the boundaries of citizenship and national belonging in search of better life opportunities. The data analyzed show the existential paradox linked to the migration–citizenship nexus that affects the lives of migrant women in Italy and their use of citizenship as a strategy to react to a blocked destiny, to follow one’s aspirations, and to rebalance gender relations. The article refines an integrated approach that considers the relationship between agency, aspiration, and capability as a broader theoretical framework within which to jointly study the dynamics of gender, migration, and citizenship as closely related, beyond the boundaries of fixed and opposite categories.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.008
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.324
Teacher spread0.288 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBorders in Globalization ReviewSame topicMigration, Refugees, and IntegrationFrench-language works237,207