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Record W2802682538 · doi:10.1177/0896920518763916

The Struggle for Recognition: The Politics of Migrant Care Worker Policies in Taiwan

2018· article· en· W2802682538 on OpenAlexaff
Yi-Chun Chien

Bibliographic record

VenueCritical Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsAllianceContext (archaeology)Government (linguistics)Social workWelfarePublic policySocial WelfareSocial policyCivil societyPolitical sciencePublic sectorEconomic growthPublic administrationSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

This article investigates how local political context—including civil society and political parties—influences the development of migrant care worker policies in Taiwan. This is particularly important in a national context where the government has actively utilized migrant care worker policies to solve the crisis in the social welfare sector. This article draws upon documentary analysis of policy debates on the proposed implementation of Long-Term Care Insurance and in-depth interviews with government officials, public service providers and non-governmental organizations to explore how the political alliances of political parties, social organizations, and interest groups affect policy outcomes. While current research focuses on the relationship between social welfare policies and the employment of migrant care workers, this article highlights the local political context and explores how political alliances have influenced the development of migrant care worker policies. This article argues that institutional path dependency and the strong policy alliance between the progressive party and social welfare organizations have stymied changes in migrant care worker policies and prevented Taiwan from further socializing the eldercare sector.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.011
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.068
GPT teacher head0.408
Teacher spread0.340 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations41
Published2018
Admission routes1
Has abstractyes

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