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Record W2091053364 · doi:10.1080/15339114.2002.9678358

Implementing Socioeconomic Measures to Tackle Economic Uncertainties in Singapore

2002· article· en· W2091053364 on OpenAlexaff
Shawn Vasoo, Kwong‐leung Tang

Bibliographic record

VenueThe Journal of Comparative Asian Development · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSocioeconomic developmentGovernment (linguistics)RecessionSocioeconomic statusEconomic growthSafety netContext (archaeology)EconomicsPoliticsPolitical scienceDevelopment economicsBusinessSociologyPopulation

Abstract

fetched live from OpenAlex

Abstract Since its independence in 1965, the government of Singapore has played a key role in orchestrating the political, social, and economic development of the city-state. Faced with the challenge of recent economic downturn, the government has reacted positively through the introduction of a number of socioeconomic measures to alleviate the plight of needy people. The main thrust of the social and economic measures implemented is to strengthen the citizens' capacities. The government thus sees human capital development even in the context of economic downturn as the critical factor to further social and economic development. Overall, policymakers in Singapore put emphasis on individual responsibility and familial support, while giving primacy to the harmonization of social and economic development. “The government, on its part, will ensure that every Singaporean has equal and maximum opportunity to advance himself, while providing a social safety net to prevent the minority who cannot cope, from falling through. This way, we can have an enduring social compact where the able can do very well, and we can use some of the wealth generated by them to subsidise and help the less able.”—Prime Minister Goh Chok Tong's National Day Rally Speech (August 19, 2001).

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.076
GPT teacher head0.342
Teacher spread0.266 · 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.

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
Published2002
Admission routes1
Has abstractyes

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