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Citizens' attitudes towards economic insecurity and government after the 2007 financial tsunami: A Hong Kong and Taiwan comparison

2012· article· en· W2162915906 on OpenAlexaff
Kate Yeong‐Tsyr Wang, Chack‐kie Wong, Kwong‐leung Tang

Bibliographic record

VenueInternational Journal of Social Welfare · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersChinese University of Hong KongUniversity of Hong KongGovernment of Jiangxi Province
KeywordsGovernment (linguistics)Financial crisisFeelingEconomic growthPolitical sciencePsychological interventionDevelopment economicsEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Wang KY‐T, Wong C‐k, Tang K‐L. Citizens' attitudes towards economic insecurity and government after the 2007 financial tsunami: A Hong Kong and Taiwan comparison The purpose of this study was to investigate people's attitudes to economic insecurity and government in Hong Kong and Taiwan after the financial tsunami of 2007. Random sampling telephone surveys were conducted in July 2009. These are the main conclusions: First, the most vulnerable groups hurt by the financial crisis were low‐income families and people who had lost their job or were afraid of losing it. This implies that the old policy issue of social stratification and the emerging policy issue of employment insecurity coexisted during the financial crisis. Second, personal experiences of economic insecurity had an influence on people's perceptions of the severity of the economic crisis at the societal level. Third, citizens had ambivalent feelings about public interventions during the crisis. Fourth, there were both convergence and divergence between Hong Kong and Taiwan with regard to attitudes to particular issues. The policy implications of these findings are discussed in the final section of this article.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.507

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 studies0.0010.000
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 head0.027
GPT teacher head0.382
Teacher spread0.354 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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