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Record W2938566432 · doi:10.1177/0020715219835891

Temporary migrants as an uneasy presence in immigrant societies: Reflections on ambivalence in Australia

2019· article· en· W2938566432 on OpenAlexvenueno aff
Claudia Tazreiter

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsAmbivalenceNegotiationImmigrationResistance (ecology)SociologyIndonesianSociocultural evolutionIrrational numberPolitical economyPolitical scienceSocial psychologyGender studiesPsychologyLawSocial science

Abstract

fetched live from OpenAlex

This article explores the status of temporariness in international migration. The focus is on the impact of temporary status on migrants’ actions, behavior, and emotional responses to the daily circumstances in negotiating everyday life. Ambivalence is evaluated as an explanatory category that allows particular insight into strategies of resistance used by temporary migrants as they navigate a host society besides maintaining connections with home. Original data obtained from in-depth interviews with Indonesian migrant workers and students undertaking temporary migration projects in Australia is discussed. The case study explored in this article identifies some of the core problems temporary migrants face as encapsulated by a deficit of rights and protections that, at the same time, are expected by members of liberal states. Temporary status turns migrants into nomadic global laborers. The article argues that actions and responses that appear to be ambivalent are far from irrational, hasty, or disloyal. Rather, migrants’ decision-making in response to the uncertain and shifting economic and sociocultural environments that they enter often comprises subtle calibrations and switching actions, observable as ambivalence, in adjusting to the unanticipated demands of a new society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.463
Teacher spread0.366 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations29
Published2019
Admission routes1
Has abstractyes

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