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Hopefully a Good Life: Cosmopolitan Chinese Migrant Families in Urban Italy

2024· article· en· W4391824453 on OpenAlexvenueno aff
Grazia Ting Deng

Bibliographic record

VenueAnthropologica · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersEuropean Commission
KeywordsGenealogyGeographyHistorySociologyEthnology

Abstract

fetched live from OpenAlex

Chinese residents have grown to be one of the most prosperous migrant groups in Italy since their mass migration from China in the 1980s. Alongside their rapid upward economic mobility, parents and children within the same families have shown generational differences in their understandings of the good life. While older generations believed that the good life means economic mobility, which is achieved through their labour and migration, younger generations’ definition of the good life, rooted in their negative experiences of racialization, is associated with social recognition. Such generational differences stem from the shifting tensions between the contested racial and national orders in association with Italy’s economic stagnation and China’s global ascendancy. Yet, both generations of these desiring subjects have manifested their own conceptions of cosmopolitan Chinese-ness to survive precarity and to aspire to a better life both economically and socially. Their family stories thus contribute to anthropological debates on how people envision their futures between hope and precarity, expectation and uncertainty, and privilege and disadvantages amid racialized class terrains, generational tensions, and geopolitical transformation of the world order.

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 score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.021
GPT teacher head0.325
Teacher spread0.303 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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