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Record W2328584347 · doi:10.1177/0961463x13491341

Time, space and care: Rethinking transnational care from a temporal perspective

2013· article· en· W2328584347 on OpenAlexaffabout
Yanqiu Zhou

Bibliographic record

VenueTime & Society · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTemporalitiesSociologyTemporalityImmigrationInequalityPerspective (graphical)Gender studiesDominance (genetics)Economic geographyGrandparentPolitical scienceGeographyEpistemology

Abstract

fetched live from OpenAlex

Against the background of unprecedented international migration, it is not clear how people’s transnational mobility and ties have intersected with the temporalities associated with places or spaces. Drawing on the data from an empirical study of the caregiving experiences of Chinese grandparents in Canada, this case study reveals the simultaneous, yet uneven, temporal impacts of transnational care on individual, familial, and transnational levels. Although the coexistence of multiple temporalities enables Chinese skilled immigrant families to mobilize care resources across generations and nation-states, the dominance of the neoliberal temporal framework also means various consequences of such transnational ‘flexibility’. I argue that rethinking transnational care from a temporal perspective helps us identify the linkages, discrepancies and contradictions between ‘global time’ and peripheral temporalities and between time and space, and thus makes visible the inequalities – in particular, the temporal inequalities – embedded in human migration and social relations on a transnational scale.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.043
Scholarly communication0.0090.015
Open science0.0020.011
Research integrity0.0010.003
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

Citations20
Published2013
Admission routes2
Has abstractyes

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