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Record W3122901588

Foreign Labor and Economic Growth: Policy Options for Singapore

2007· article· en· W3122901588 on OpenAlexaff
Weng Tat Hui, Aamir Rafique Hashmi

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomicsLabour economicsProductivityPaceImmigrationLabor demandTotal fertility rateDevelopment economicsPopulationEconomic growthWagePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Prior to the Asian economic crisis in 1997, Singapore's official projected medium-term GDP growth target was set at 7% per annum. Since then, the targeted growth rate has been reduced to 5%. This paper examines the implications of the 5% growth target on the labor requirements of the Singapore economy. It is shown that the projected resident labor force will not be able to keep pace with the increased labor demand and the share of foreigners in the labor force will increase significantly even under the most favorable scenario. Some implications of the increased dependence on foreign labor in Singapore are discussed. With permanent immigration fixed at the current level, various policy options and their effects on the demand for foreign labor are considered. These include improving labor productivity, raising the total fertility rate, increasing labor force participation of older workers and lowering the targeted rate of economic growth.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0210.001

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 head0.012
GPT teacher head0.308
Teacher spread0.296 · 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 designNot applicable
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

Citations0
Published2007
Admission routes1
Has abstractyes

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