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Record W4255927196 · doi:10.1108/oxan-db250466

Investment will drive solid Taiwanese growth in 2020

2020· other· en· W4255927196 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2020
Typeother
Languageen
FieldSocial Sciences
TopicAsian Industrial and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsMandateInvestment (military)Quarter (Canadian coin)ChinaGeneral partnershipPopulationBusinessTrade diversionPopulation growthEconomicsInternational tradeEconomic growthInternational economicsDevelopment economicsFree tradeGeographyPolitical scienceFinanceInternational free trade agreement

Abstract

fetched live from OpenAlex

Subject Taiwan growth prospects. Significance Taiwan’s GDP grew by 3.38% year-on-year in October-December 2019. This is an acceleration from 2.6% year-on-year growth in the second quarter to 3.0% in the third. Consumer spending has grown steadily, while investment reshoring and exports to the United States have grown even more strongly due to trade diversion designed to mitigate the impact of US-China trade tensions. Impacts Taiwan is not currently part of the Asia Regional Comprehensive Economic Partnership and its exclusion may limit its trade opportunities. If Taiwan learns from Japan’s experience of adjusting to an ageing population, automated social services could emerge as leading sectors. The president has a renewed mandate to introduce reforms aimed at raising wages and creating jobs, especially in high-skill industries.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.096
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0960.044

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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
GenreCommentary

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
Published2020
Admission routes1
Has abstractyes

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